From Preferences to Prejudice: The Role of Alignment Tuning in Shaping Social Bias in Video Diffusion Models
Authors: Zefan Cai
Haoyi Qiu
Haozhe Zhao
Ke Wan
Jiachen Li
Jiuxiang Gu
Wen Xiao
Nanyun Peng
Junjie Hu
Venue: Accepted by TMLR ()

Abstract: Recent advances in video diffusion models have significantly enhanced text-to-video generation, particularly through alignment tuning using reward models trained on human preferences. While these methods improve visual quality, they can unintentionally encode and amplify social biases. To systematically trace how such biases evolve throughout the alignment pipeline, we introduce VideoBiasEval, a comprehensive diagnostic framework for evaluating social representation in video generation. Grounded in established social bias taxonomies, VideoBiasEval employs an event-based prompting strategy to disentangle semantic content (verbs and contexts) from actor attributes (gender and ethnicity). It further introduces multi-granular metrics to evaluate (1) overall ethnicity bias, (2) gender bias conditioned on ethnicity, (3) distributional shifts in social attributes across model variants, and (4) the temporal persistence of bias within videos. Using this framework, we conduct the first end-to-end analysis connecting biases in human preference datasets, their amplification in reward models, and their propagation through alignment-tuned video diffusion models. Our results reveal that alignment tuning not only strengthens representational biases but also makes them temporally stable, producing smoother yet more stereotyped portrayals. These findings highlight the need for bias-aware evaluation and mitigation throughout the alignment process to ensure fair and socially responsible video generation.

Published in Transactions on Machine Learning Research (02/2026)
From Preferences to Prejudice:
The Role of Alignment Tuning in Shaping Social Bias in Video Diffusion Models
Zefan Cai*!, Haoyi Qiu"? Haozhe Zhao Ke Wan Jiachen Li Jiuxiang Gu, Wen Xiao Nanyun Peng Junjie Hull
ze sefnonidgmmill com, hamjiqiu@ess uew.aaoess..llleeooooooooo edu, haozhee6@0llinoos.cce
University of Wisconsin Madison
University of Colifornia, Los Angeles
University of Ilinois Urbona- Champaign
Universily of California, San Diego
Universily of California, Sunla Barbara Microsoft
https: // github. com/ WeeaannCaa/Vieeeeeeeeeaa
Reviewed on OpenReview: https: operrevee..eee....o net/ forum? id- C0y.u86j
Abstract
Recent advances in video diffusion models have significantly enhanced text-to-video generation, particularly through alignment tuning using reward models trained on human preferences. While these methods improve visual quality, they unintentionally encode and amplify social biases. To systematically trace how such biases evolve throughout the alignment pipeline, we introduce VIDEOBIAsEVAL, a comprehensive diagnostic framework for evaluating social representation in video generation. Grounded in established social bias taxonomies, AISEO employs an event-based prompling stralegy disentangle semantic content (verbs and contexts) from actor attributes (gender and ethnicityy.. It further introduces multi-granular metrics to evaluate (1) overall cthnicity bias, (2) gender bias conditioned on ethnicily, (3) distributional shifts in social attributes across model variants, and (4) the temporal persistence of bias within videos. Using this framework, we conduct the first end-to-end analysis connecting biases in human preference datasets, their amplification in reward models, and their propagation through alignment-ture tund video diffusion models. Our results reveal that alignment tuning not only strengthens representational biases but also makes them temporally stable, producing smoother yet more stercotyped portrayals. These findings highlight the need for bias-aware evaluation and mitigation throughout the alignment process to ensure fair and socially responsible video generation.
1
Introduction
Recent advances vidco diffusion models have led to substantial improvements in generating high-quality videos from natural language prompts (Chen al., 2024a] Wang et al. 2023a Yuan et al., 2024 Li al.,
2024 enabling wide range of applications in creative media, education, and professional simulation (Cho ct a.l. 2024. Miller et al.] 2024). To further enhance visual fidelity, coherence, and controllability many statc-of-the-art video generation systems now incorporate alignment tuning techniques, most prominently
learning from human preferences (Wu et al. 2023 Xu et. al.) 2024 Li et. al. 2024 Yuan et al. 2024 Liu
et al.
2024a Prabhudesai et al.] 2024 Black et al. 2023
Ma et al. 2025). These approaches typically rely
"Equal contribution, ordered by last
Equal advising.

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Published in Transactions on Machine Learning Research (02/2026)
Social Attributes Representation
A person atw drinking artin gm,, standing workout equipment with other people exercising around
White
Man
Frame 13
Video Social Attnibtts
Middle Eastern
Man
Representation
VLM-based Evaluators
White
Frome
Man
Woman
Aggregated Results from al Thene VLA--baed Evalustors
Frame1
White
Man
Generated Video
Video Frames
Results from Qwenna VLyE
VideoBiasEval: Event-Centric Bias Evaluation
Image Reward Datasets
Human Preference Blases
Event Prompting
Pécle-
HPbv2
Both hottsdds
Granular
Video Diffusion Models
Blas Evaluation
Aligamentt Tuning
HōmAn
miAr
The others
Whild
Schnioity-wwor Gendeer Bins
Image Reward Models HPDSSu2. 5, Piolosssort
Reward Preference Blases
Generated Videas
PickeSsort/
VLM-based Evaluators
ethoisiry wlas
Modells
The sthers
White
Alignment-tuned Video Diffusion Models VCM VC2 + HPNSSV2.0, HFSV2.1, FicksSoort
Video Diffusion
Models Generation Blases
Figure 1: Overview of our work: (1) We introduce VIDEOBIASEVAL, a social bias evaluation framework for video generation that leverages event-based prompts and multi-granular metrics to assess ethnicity and gender bias (bottom left, 3 The framework represents videos through social attribute annotations (top), where visual-language models (VLMs) infer actor attributes such as gender and ethnicity across frames and aggregate them for bias quantification $3.3). (2) We conduct the first comprehensive analysis of how image- based reward models, shaped by human-labeled preferences, influence the distribution of social attributes in diffusion- generated videos, disentangling human preference biases, reward preference biases, and their downstream impact on video diffusion model generation biases (bottom right, 5.1 5.2 and $6
on reward functions trained from human-labeled comparisons (wu et al., 2023 Kirstain al. 2023a Xu
ct al. )024), ; often operating on framo-level image-level judgments to guide post-training optimization.
In this work, we use the term aligned models to refer to video diffusion models that undergo post-truining optimization to better match human preferences beyond likelihoodd based training. In practice, such alignment is commonly achieved via reward-weighted fine-tuning or RLHF-style procedures, whore a learned preference or aesthetic reward model trained on human comparisous is used to guide further optimization of a base video diffusion model. Conceptually, this process follows a general pipeline of base video diffusion model preference or reward model + post-training optimization. Throughout this paper, we contrast aligned models with their unaligned counterparts, which trained solely using likelihood-base objectives and serve controlled baselines for isolating the effects of alignment.
While aligmment tuning has proven effective at improving perceptual quality and temporal coherence, its reliance on subjective notions of "preference" introduces a critical yet underexplored risk. Human preference judgments- often collected without explicit consideration of cultural, social, or demographic diversity- may encode systematic biases that are subsoquently inherited and reinforced by reward models and downstream generative systems (Qiu et al. 2023). As result, alignment tuning can inadvertently shape not only what a model generates well, but also who is represented, how often, and in what roles. In this work, we investigate central and underexamined question: how alignment tuning influences the distribution of social attributes in video diffusion models.
Exploring this research requires a holistic evaluation framework one one that incorporates a probing method elicit social attributes from video diffusion models, metrics to quantify the distribution of social biases within these models, and an analysis protocol capable of tracking changes in social attribute distributions before and after alignment. However, existing evaluation frameworks (Huang et al. 2024 Liu et al. 2024b
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Sun et al. 2024) fall short in detecting and analyzing social biases due to three key limitations: (1) their reliance on prompts that do not adequately represent diverse social identities, thus limiting the analysis of how models portray or misrepresent these attributes; (2) the lack of comprehensive identity coverage and specific metrics, which hinders the ability of prior work to track the impact of alignment techniques on the distribution of social altributes; and (3) the absence of a dedicated method to track shifts social attribute distributions before and after the application of alignment techniques.
We address these limitations by introducing VIDEOBIASEVAL ($3) a comprehensive evaluation framework for analyzing social bias in video diffusion models. The framework leverages event- based prompting and builds on established social bias taxonomies (Zhao et al.) 2017 Hendricks & Nematzadch 2021 Cho et al. 2023 Qiu ltt al. 2023), allowing for precise control over both verb types and actor identity attributes. This separation of social identity from semantic content enables robust and interpretable assessments of how models represent social attributes across varied contexts. Building on prior work in alignment and fairness within generative systems (Luccioni et al.] 2023 Shen et al.) 2023), we specifically focus on gender and ethnicity, two social dimensions with comparatively well-defined evaluative boundaries. Furthermore, the framework introduces multi-gramular metrics that designed to assess (1) ethnicity bias, (2) gender bias conditioned on ethnicity, (3) shifts in social attribute distributions across different models, and (4) the temporal persistence of bias within videos. Built on this foundation, our analysis traces how social attribute distributions evolve throughout the alignment tuning pipeline.
We begin with an examination of demographic preferences embedded in human preference datasets, specifically HPDv2 (Wu et al.) 2023) and Pick-a-Pic (Kirstain et al., 2023a) ($5.1). Next, we investigate how these patterns are inherited by image reward models, including HPSv2.0, HPSv2.1 [wu et al., 2023 aand PickScore Kirstain et al. 2023a (5..)) Finally, we fine-tune a video consistency model distileed from VideoCrafter-2
Chen et al. 2002) using different image reward models ($6), enabling a detailed comparison of video outputs before and after alignment. This analysis reveals how alignment tuning reshapes the distribution of social attributes in generated content. Experimental results show that both human preference datasets exhibit non-neutral gender preferences and a strong imbalance lavoring While representations. Reward models trained on these datasets inherit and amplify these social biases, which are then propagated through alignment-tuned video diffusion models. Alignment with male- or femalo-preferred reward models systematically shifts gender portrayals, while ethnic representation remains uneven across groups. Moreover, our Temporal Attribute Stability (TAS) analysis indicates that aligmment tuning improves the consistency of social attribute portrayals over time but can also make biased representations more persistent and visually stable. These findings demonstrate that alignment tuning simultancously enhances vidco quality and coherence while reinforcing or stabilizing existing social biases. They highlight the need for bias-aware evaluation and alignment strategies throughout the generative pipcline to ensure equitable and socially responsible video generation.
Furthermore, examine whether controllable image reward datasets can be intentionally constructed by manipulating the distribution of social attributes ( We then assess whether training reward models on such curated datasets enables video diffusion models to generate outputs with controllable bias represen- tations, thereby offering & potential path toward more equitable generative systems. Finally, we provide a comprehensive analysis of the changes in reward model preferences across 42 events and the resulting shifts in video model bias before and after alignment tuning. Building on these findings, we further offer guidance for addressing observed biases, outlining how targeted data composition and counter-biasee reward modcling can serve as effective strategies for mitigating representational disparities in future generative video systems.
We make three key contributions: (1) We introduce VIDEOBIASEVVLL a comprehensive framework for evaluating social bias in video generation, which leverages event- based prompting and multi-granulat metrics to assess ethnicity bias, gender bias conditioned on ethnicity, and temporal stability of social attributes across videos. (2) We present the first end-to-ent analysis connecting human preference datasets, reward models, and aligrment-tung -uueed video diffusion models, revealing how social biases are inherited, amplified, and stabilized throughout the aligument pipeline. (3) Through systematic experiments, we demonstrate that preference alignment not only improves perceptual quality and temporal coherence but also reshapes- and in some cases reinforces the social composition of generated content. Building on these findings, we offer guidance for addressing observed biases through controllable preference modeling, showing how targeted
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data composition and counter- counter-biased reward design can effectively steer video diffusion models toward more equitable generative behavior.
2
Related Work
T2V Evaluation. Recent evaluation benchmarks such as VBench (Huang al. 2024), EvalCrafter (Liu
et al.) 2024bb, and "2V-CompBench (Sun et al. 2024) evaluate text-to-video models using metrics like Fréchet Video Distance [Unterthiner et al. 2019), CLIP-Score (Hessel et al. 2021), and object consistency,
yet they overlook who is depicted and how identities are portrayed. GRiT- metrics (Wu et al. 2025 may verify that "doctor" appears, but fail to flag when all doctors are white men. CLIP-based alignment rewards textual fidelity but ignores demographic balance. To ensure fair and trustworthy evaluation, T2V benchmarks must move beyond surface-level metrics and explicitly audit the distribution of social attributes across outputs. Our work meets this need by introducing an event-centric framework that quantifies gender and ethnicity-aware biases throughout the entire T2V generation pipeline.
Bias Evaluation in Generative Models. Most existing studies on social bias in text-to-image language generation focus on static, single-frame outputs such as portraits or isolated object seenes. Approaches like (Howard et. al.)
StableBias (Luccioni et al. 2023), DALL-Eval (Cho et al. 2023), and SocialCounterfactuue 2024) primarily tally identity frequencies but seldom examine what those identities are portrayed doing. Even recent benchmarks that track demographic representation often evaluate each image independently, which conceals recurring patterns such as the tendency to depict men in authoritative roles and women supportive ones. By neglecting to analyze actors, verbs, and context jointly, these evaluations fail to capture role-specific stereotypes and cannot reveal bias in narrative or temporal settings. We address this limitation by auditing at the event level, disentangling actor attributes from verbs and environments to uncover how social representation shifts across different scenarios.
3 VidvaBia
We introduce VIDEOBIASEVAL, comprehensive framework for evaluating social biases in video generation models. Our approach leverages event-based prompting, where we systematically vary the gender and ethnicity of characters across a diverse set of real-world events ($3.1). Using these structured prompts, we generate videos with state-of-the-art diffusion models ($3.2 To quantify consistency and fairness in identity portrayals, we extract social attribute representations from the generated videos and perform multi-granular evaluation across event categories ($3.3). Figure illustrates representative prompts and demnnnstrate how social attributes are captured and analyzed from the generated outputs.
Prompt Template
A/An [actor] is baking a batch of cookies in cozy kitchen, with warm light and the aroma of vanilla filling the air.
Actors Models
Person
Person
Indian Person
Southeast Asian Person
Video-Crafter-V2
T2V-Turbo-VI
T2V-Tubbo-Vi
T2V-Turbo-VI
Random Four Frames of Generated Videos
Social Attributes Representations
(Man, White)
(Man, White)
(Man, Indian)
(Woman, Southeast Asian)
Figure 2: Illustration of videos generated by different diffusion models using varied prompt templates that specify actor attributes as detailed in $3.2 The main character's social attributes, including gender and ethnicity, are extracted using our proposed VLM-based evaluation method described in $3.3
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3.1 Event Definition
We investigate whether video generation models exhibit social biases in their portrayals of events, focusing how different actors are visually represented while performing verbs. Such biases often appear as imbalanced portrayals across gender or ethnic groups, reinforcing stereotypes and undermining fairness patterns
documented in prior work (Bolukbasi et al.) 2016 Sun & Peng 2021 Zajko 2021). To systematically examine these effects, we represent each event as a tuple (p, a, c), where an actor p performs an verb a within a context c. Our analysis centers on socially associated verbs those historically tiod to identity-related
stereolypes following prior studies (Zhao al., 2017 Garg et al.) 2018 Cho al.) 2023 Qiu et al.) 2023)
This formulation enables to assess how demographic attributes infiuence visual depictions across generated videos and to quantify systematic biases in model behavior.
Controlled Attribute Space. Our altribute design intentionally balances coverage and control. We analyze four gender categories and seven ethnic groups combined with 42 verbs-choin grounded in established social bias taxonomies and prior benchmark conventions. This deliberately bounded setup enables the first end-to-end tracing of bias propagation in video generation, allowing clear attribution and interpretability while maintaining reprodueibility. Expanding to open-ended intersectional attributes is a promising next step; however, a well-defined and theoretically anchored scope is crucial for isolating reprosentational disparitics before scaling to unconstrained scenarios.
Actors. Each actor (p) is depicted with gender and ethnicity attributes to facilitate structured analysis of social bias. For gender, we adopt the four categories proposed by [uccioni et al. (2023): man, woman, the neutral term "person," and non-binary person. Although inclusive, this schema cannot. capture the full diversity of gender identities but offers clear evaluative boundaries for controlled analysis. For ethnicity, we employ seven groups White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and followi- Karkkainen & Joo [022]] and U.S. Census Bureau conventions. While these categories aim to be inclusive, they are socially constructed and not intended to be exhaustive or universally representative.
Verbs. We examine 42 verbs (a) bake, bike, call, clean, climb, cook, cough, cry, drink, drive, eat, ezercise, fish, hit, jump, kick, kneel, laugh, lift, paint, pick, pitch, pray, read, ride, row, run, shop, shout, sit, skate, sleep, smile, stand, stare, stretch, study, sweep, throw, walk; wash, work- priv-oorvi identified in the literature as statistically associated with particular genders or ethnic groups (Zhao et al. 2017 Garg et al., 2018 Cho et al. 2023 Qiu et al. 2023). These verbs were selected not only for their well-documented social associations but also for their high visual distinctiveness and ease of depiction in short video clips, which ensures reliable annotation and interpretability of model outputs. This carcfully curated yet socially meaningful verb set establishes a reproducible foundation for future extensions toward more complex, culturally specific, or temporally dynamic event scenarios.
3.2 Event-Based Prompting Protocol
Settings # of Prompts Examples
A person baking cook- ies in a cozy kitchen, with warm light and the aroma of vanilla filling the air.
To generate diverse yet systematically compara- ble evaluation prompts, we adopt fully determin- istic event template: "A/An [actor] is [verb]-ing [context]." " Crucially, neither the actor nor the con- text is generated by an LLM at runtime. Instead, all prompts are instantiated from a fixed, predefined set to ensure reproducibility and controlled comparisons. The [verb] slot spans the 42 curated activities, while [context] introduces situational variety without al- tering the semantic identity of the verb. To disentan- gle the influence of demographic attributes, we define two prompting conditions: (1) Person-onlyy where the [actor] is fixed to "person" (one prompt per verb, 42 total); and (2) Ethnicity+Person where a predefined ethnic descriptor is deterministically prepended to "person" (e.g., "An Fast Asian person"), "), yielding 294 prompts formed by permuting the same
Person Only
42
An East Asian person is baking cookies in a cozy kitchen, with warm light and the aroma of vanilla fill- ing the air.
Ethnicity + Person
294
Table 1: Statistics of social bias evaluation prompts for video generation. Each prompt explicitly highlights the actor's ethnicity (when specified), the verb, and the surrounding context, providing a structurod basis for analyzing social representations in generated videos.
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42 verbs across seven ethnic groups. Table I summarizes the prompt distribution and presents illustrative examples. Because the ethnicity+person condition inherently encodes ethnic information, an additional ethnicity-only setting is unnecessary for isolating ethnicity effects.
Each instantiated prompt is directly used to query the target video generation models, producing a fixed number of videos per prompt under identical decoding settings. Our objective is to evaluate how video generation models represent actors when demographic attributes are underspecified under a fixed verb. For example, under the person-only condition, we examine which gender or ethmicity attributes models implicitly assign in the absence of explicit demographic cues. Under the ethnicity+person condition, explicitly provided ethnic descriptors interact with other inferred attributes, such as gender. Importantly, demographic at attributes are not introduced, constrained, or inferred during generation. All demographic analysis is conducted post hoc by applying an ensemble of Aision-language models to the generated video frames to infer actor attributes ($3.3). Overall, this controlled event-prompting framework ensures that obscrved disparities can be reliably attributed to demographic conditioning rather than uncontrolled contextual drift an essential property for reproducible bias auditing in generative video models.
analyze how
3.3 Multi-Granular Event-Centric Bias Evaluation
We propose a multi-granular evaluation method that captures both foue-grained frume dynamics and aggregated video-level fairness, enabling consistent assessment of how demographic portrayals emerge and persist over time. This design allows us to systematically analyze social biases in diffusion- vidoos across diffcrent temporal and representational gramularities, revealing not only who is represented but also how consistently identities are maintained throughout the video.
Social Attribute Representations. We use three open-source assion-lanuuage models (VLMs) Q Qwen2- VT-7B (Wang el al. 2024a], Qwen2.5-VI--B (Yang el. al. 2024 and InternVL.2255-BD (Chen et al. 2024b) as automated judges to perform frame-wise classification of social attributes. For each generated video, we uniformly sample 16 frames. This sampling rate aligus with the standard inference defaults of the evaluated models (e.g., VideoCrafter);; our preliminary experiments indicated that reducing the frame count significantly compromised attribute detection stability. We prompt the VLMs to independently infer the depicted gender and ethnicity in cach frame, where cach model outputs gender label g E G = (man, woman) and cthnicity label E E (White, Black, Indian, East Asian, Soatheast Asian, Middle Eastern, Latino}. Frame-level predictions are first aggregated within each model via majority voting to obtain video-level labels, and then fused across models through an ensemble strategy. We employ this multi-model "VIMssaasjjudgss" ensemble because initial manual inspections revealed that individual VLMs frequently exhibited instability or hallucinated attributes. By ensembling distinct architectures rather than simply resampling a single model, we effectively cross-validate results to enhance robustness and mitigate idiosyneratic biases (Qiu et al. 2024b). Representative classification prompts and example video outputs are shown in Figure 2 The resulting frome- and video-level social-attribute representations enable systematic evaluation of how well each generated video aligns with the intended demographic attributes specified in its prompt. The upper portion of Figure illustrates this pipeline for deriving structured social-attribute representations. To ensure reliability and fairness, we further conduct human human-model correlation analyses detailed in $
[]
Temporal Attribute Stability. To complement static framo-level evaluation, we introduce a new temporal metric that directly quantifies the intra-video slability of social altribute representations over time. For each video, the Temporal Attribute Stability (TAS) score is defined as the percentage of frames whose classified attributes match the final majority-voted label for the video. A high TAS indicates temporally coherent and stable demographic portrayal, while a low TAS reflects attribute "flickering" or inconsistent identity depiction across frames a critical temporal artifact in biased or unstable generation processes.
Beyond frame and temporal consistency, we further assess whether models generate socially balanced representations when aggregating across videos. The following video-level metrics quantify gender and ethnicity fairness across events and demographic groups
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Ethnicity-Aware Gender Bias. To assess how video generation models portray gender across different ethnic groups, we employ the Proportion Bias Score for Gender (PBS&)) H For each verb and ethnicity group, PBSc is defined as PBSg- = (Nman - -NNooann//Notad E [-1,1], 1], where Nman and Nwoman denote the number of representations depicting men and women, respectively, and Nootal is their sum. For interpretability, a PBSg value of 0.5 implies that male representations occur at. three times the frequency of female representations (i.e., Nman = womnm,,,, while a value of -0.5 indicates the inverse ratio. A positive PBSg indicates bias toward male representations, negative value indicates a bias toward female representations, and values near zero suggest balanced gender representation. Importantly, PBSg z 0 is not treated as claim about real-world gender distributions, which may legitimately vary across verbs, ethnicities, and sociocultural contexts. Instead, we adopt this zero-centered target as a diagnostic benchmark for controlled bias auditing rather than a factual or normative ground truth. By using neutral "person" "-based prompts that explicitly decouple verbs from identity attributes, deviations from PBSg R 0 quantify the extent to which models internalize and reproduce gendered stercotypes associated with spocific verbs. This framing follows establishod practice in fairness and representational harm analysis, where uniform or parity-based baselines are used to expose systematic skew in generative models rather than to assert empirical correctness, following prior work on representational bias in generative models. By computing PBSg under the ethnicity+gender condition, gender portrayals vary within each ethnic group reveellg whether models exhibit consistent gender balance across demographics or whether gender disparities are unevenly amplified for specific ethnicities. In this sense, PBSSg serves as a sensitive probe of stereotype amplification across than an assertion that identical gender proportions are universally appropriate.
capture how
verbs, rather
Ethnicity Bias. To evaluate how video generation models represent different ethnic groups, we employ two complementary metrics: the Representation Deviation Score for Ethnicity (RDS.) (Feldman al. 2015 Mehrabi et al. 2021) and Simpson's Diversity Index (SDI) (Simpson 1949). For each ethnic group e E E, we define its representation proportion as P. = Ne/Niotal. where Ne denotes the number of outputs identified as ethnicity e, and Niotal is the total number of outputs with an identifiable ethnicity. The first metrie, RDS,- = Pe- 10--// -,, quantifies how much each group's representation deviates from an ideal uniform distribution, where 1/ [F] reflects perfectly balanced coverage across all groups. For interpretability, given seven ethnic groups, the ideal balanced probability is 1/7 14.3%. An RDS, value of 0.769 implies that a specific group appears in 0.769 + 0.143 = 91.2% of the generated vidcos more than six times the expected frequency. A posilive RDS, indicates overrepresentalion, while a negalive value signals underregresentlaion.. Importantly, this uniform reference is not intended to reflect real-world demographic proportions, which may legitimately vary across regions, cultural contexts, or downstream applications. Instead, we adopt it as a diagnostic benchmark for controlled bias auditing under the neutral person-onll -orloonnonlyoooo setting. Because prompts in this setting intentionally remove context and identity cues, deviations from uniformity reveal the extent to which generative models internally favor or suppress specific ethnic identities, rather than reflecting external demographic statistics. This framing follows prior work in fairness and representational harm analysis, where parity-based baselines used to expose systematic skew in learned representations rather than to assert empirical correctness. Complementing this, the overall diversity of representations is mcasured by Simpson's 's Diversity Index, SDI = - Eeer Pe which captures the probability that two randomly selected outputs belong to different ethnic groups. Higher SDI values indicate more diverse and balanced distributions, while lower values suggest concentration around a few dominant groups. Together, RDS, and SDI provide a comprehensive perspective: RDS. highlights who is over- or underrepreseented, whereas SDI reficcts how balanced the overall represenlation landscape is. These metries jointly reveal whether generative models fairly portray global ethnic diversity or perpetuate skewed and homogeneous visual patterns.
Bias Shift. Finally, we analyze bias shift between unaligned and aligned models to understand how alignment methods influence social fairness at the video level. For each metrie, we compute APBSg, ARDS., and ASDI to quantify directional changes. Shifts toward more balanced gender ratios, reduced ethnic skew, or higher diversity indicate improvement. This complete evaluation suite spanning frame dynamics, temporal stability, and video-level demographic fairness provides a holistic understanding of bias formation and mitigation in diffusion-based video generation.
We exclude gender bias from this analysis because, in the absence of explicit cthnicity specifications, generative models predominantly produce representations of White individuals (Figure 14 Consequently, analyzing gender alone effectively reduces to examining gender bias within the White demographic (Figure
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Average
White
Black
Latino
East
Asian
Southeast Asian
India
Middle
Overall
Modele
PBSO
PBSG
RDS
PBSo
RDS
PDSG
RDS
PBSG
RDS
PDS0
RDS
PBBSc
RDS
PBSS
RDS
SDI
ModelScope [u]
0,0815
0.5683
0.7690
0.3912
-0.1952
0.6308
+0.1952
0.1106
-0.1810
0.4811
0.3938
0.4833
-..1976
0.0536
Vidro (x)
0.5295
0.6384
0.7833
0.5114
40.1876
0,6729
-0.122)
0.4282
-0-1976
0.5020
0.4878
0.5388
-0.1932
0.0267
+0.0480
-0.0000
+0.0143
40.12022
-0,0024
40.0421
+0.0023
-0.0124
-0.0166
+0.0400
+000540
+000500
+0.0024
-0.021
Video Craßee- V2 (u)
0.7581
0.7485
0.0905
0.6167
-0.1905
0.8599
00.1952
0.0976
-0.1500
0.8272
0.8082
0.7560
..1548
0.1252
Turbourbo V1 (a)
0.8306
0.8713
0,8381
0.5095
0.8599
00.2176
0.7762
-0.2120
0.8929
0.7762
0.7664
-0.1476
0.1119
1000225
00.1228
-0.0524
10.1928
0.0000
-0.0524
10.0786
-0.0923
|0.0657
-0.0270
10.0104
+0.0072
-0.0133
Table 2: Distributions of social attributes in two pairs of unaligned (u) and aligned (a) video diffusion models. Each entry reports the average score computed across 42 verbs. For PBSg, the sign of each value denotes the absolute direction of gender bias for that model: positive values indicate man-preference, negative values indicate woman-pree prmman-reefrrnce,, and values closer to zero indicate reduced gender skew under this metric. Accordingly, non-zero PBSg values in unaligned baseline models (ModelScope and VideoCrafter- V2) reflect inherent pre-alignment bias, rather than bias-free behavior. For RDS,,, positive values indicate overrepresentation of a given ethnicity, while negative values indicate underrepresentation, again applying to absolute scores rather than post-alignment shifts. SDT measures overall ethnic balance, where higher values indicate greater diversity and more uniform representation across ethnic groups. Missing values are marked as " and indicate cases where the model generated zero recognizable instances of the corresponding ethnicity under the person-onls condition (i.e., when ethnicity is not explicitly specified), reflecting a severe lack of diversity that prevents metric computation for those subgroups. The A rows report alignment-induced changes relative to the corresponding unaligned models. For APBSc, we annotate man-preference shifts with (+) and coman--reference shifts with (-). For ARDS,, (+) and (-) denote shifts toward overrepresentation and underrepresenttatinn, respectively.
"_
4 Social Biases in Video Generative Models
We apply our proposed evaluation framework to four state-of-the-art video diffusion models with varying alignment strategies. In this work, we use the term aligned models to refer to video diffusion models that undergo post-training optimization to better match human preferences, typically via weight-we fino-tuning or Rsyle procedures using Icarned preference or aesthotic reward models. Concretely, this aligument process follows a general pipeline of base video diffusion model + preference/rewaan /reward model - post- training optimization. Under this definition, the aligned models include Instruct (Yuan et al. 2024 which builds ModelScope (Wang et al. 2023a) and is aligned using HPSv2.0, well as T2V-Turbo- V1 (Li et al. 2024), which builds on VideoCrafter-2 (Chen et al. 2024a) and is aligned with HPSv2.1, InternVid2-S3. (Wang et al.) 2024b), and ViCLIP (Wang et al. (023b)). Their unaligned counterparts, ModelScope and VidcoCrafter-2, serve as basclines for controlled comparisons, allowing us to isolate the effects of alignment from those of base model architecture.
To compute the social bias distribution, as outlined in $3 we generate videos with each prompt 10 times per model with different random seeds and average the results to reduce sampling variance. Table 2 reports two social bias metrics: ethnicity-aware gender bias (PBSa) and ethnic representation distribution (RDS, and SDI). Additional analysis across 42 verbs, the effect of training data (WebVid-1OM BBin et al. 2021 and a statistical analysis (Table 9 appear in Appendix A
Athnicity-Awan Gender Bias. We evaluate gender portrayals under the ethnicity+person condition using the PBSg metric. Our analysis reveals a consistent male bias, with average PBSg values remaining above zero across all ethnic groups. Notably, aligrmmet. luning appears to amplify this imbalance. Instruct Video and T2V-Turbo-V V1 exhibit PBSg increases of 0.04 and 0.0725, respectively, indicating that preference-based fine-tuning may worsen gender disparity rather than alleviate it. Figures to 13 present the detailed PBSg scores across 42 verbs for each ethnicity.
Ethnicity Bias Under the person-only condition, we analyze models' representation balance using the previously defined RDS, and SDI metrics. All models show a pronounced overrepresentation of White individuals, though the magnitude varies. ModelScope exhibits the strongest imbalance (RDSwate = 0.769, SDT 0.0538), which is further amplified by alignment tuning in InstructVidee (RDSwnite 0.783, SDI
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0.0267). VideoCrafter-2 achieves moderately improved balance (RDSwhite 0.6905, SDI = 0.1252), while T2V-Turbo-VI further reduces White dominance (RDSwhite 0.6381) but at the cost of lower diversity (SDI = 0.1119). Overall, while alignment tuning may alleviate certain ethnic skews, it can also suppress demographic diversity, suggesting a trado-off between bias reduction and representational variety. Figure 14 show the ethnicity bias across 42 verbs.
Human Evaluation. To ensure the reliability of our VLM-based evaluators, we conduct human verification across 400 generated videos annotated by three independent annotators for gender and ethnicity. All annotations were performed by three independent researchers with prior experience in social bias analysis. Annotators followed standardized written guidelines and received task-specific training grounded in established gender and ethnicity categorization conventions used in prior bias evaluation work. The annotation task was limited to verifying whether the generated content correctly reflected the prompted demographic attribute, rather than eliciting subjective or normative judgments. Our ensemble of three open-source VLMs ILMMsaggregatod via majority gatingsshors strong alignment with human judgments, achioving Pearson correlations of 0.89 (gender) and 0.73 (ethnicity). While the ethnicity correlation reflects residual disagreement, further analysis reveals that many discrepancies arise from differences between human holistic judgment and the evaluator's ss frame-level precision: flecting but visually valid attributes (e.g., faces visible for only a few frames in fast- motion clips) are sometimes missed or inferred by humans but explicitly captured by the VLM pipeline, indicating higher recall rather than systematic false positives. Agreement. metries further confirm high consistency: for video-level verification across the full pool of 400 videos, Cohen's Kappa reaches 0.91 (gender) and 0.78 (ethnicity), with Fleiss' Kappa of 0.92 and 0.82, respectively, indicating strong to near-perfect inter-annotator agreement. To examine whether evaluator disagreement affects downstream conclusions, we further conduct sensitivity analysis with VLM predictions. We observe high consensus rales (99.0% for gender and 83.3% for ethnicity), and critically, all core bias metrics remain statistically stable when restricting to this subset (e.g., PBSg: 0.846 + 0.863, = 0.68; SDI: 0.745 - 0.761). This non-significant difference confirms that residual evaluator uncertainty does not distort the reported bias measurements. Finally, we emphasize that human verification is used to ground and sanity-check the automated evaluation pipeline, rather than to replace large-scale evaluation. While larger-scale human annotation would be ideal, it is constrained by substantial labor costs and follows established practice in prior work that validates model-based judges using small-scale but high-consensus human review. Taken logether, these results demonstrate that the VLM ensemble provides a reliable, scalable, and human-aligned evaluation mechanism for large-scale social attribute analysis.
high-agroement subsct where human majority votes perfectly align
These findings lead to our central research question: how does alignment tuning shape the distribution of social attributes in video generative models? To answer this, we (1) analyze demographic distributions embedded in the image reward datasets ($5.1), (2) examine the social biases the trained reward models (5.2 (3) assess how these biased reward models influence the representation of gender and ethnicity in video outputs when used for alignment tuning ($6).
5 Social Biases in Image Reward Datasets and Reward Models
Using image-based reward models has become the de facto and state-of-the-art approach for alignment. tuning
in video diffusion models (Wu et al., 2023 Xu et al. 2024 Li et al. 2024 Yuan et al. 2024 Liu et al. 2024a Prabhudesai et al., 2024 Black et al. 2023 Ma el al. 2025). Because these reward models are trained on static images yet guide learning in temporally coherent video generation, their inherent social biases can propagate and even amplify across frames. Undorstanding such bias transfer from reward datasets to trained reward models is therefore crucial not only to uncover demographic disparities embedded in human-labbled image preferences, but also to reveal how these biases shape the temporal evolution of social attributes in generated videos.
5.1 Preference in Image Reward Datasets
We analyze two widely used image reward datascts to investigate human preference biases: HPDv2 (Wu) et al. 2023) and Pick-a-Pic (Kirstain et al. 2023b). For each dataset, we extract gender, ethnicity, and verb attributes from image captions using GPT-44-mini, and classify attributes from images using three VLMs
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Picka
1.00
0.75
0.25
Estnic ty
0.09
Black
IPD
cest astan
India
2.00
infro
0.75
50 southhoast astnn
duli eastum
white
Actions
Figure 4: Image reward datasets ethnicity preference across 42 verbs.
Jiwen2-VL-7B. Qwen2.5-VL-7B. InternVL2.5-8BB. We then aggregate the social attributes from both caption and image modalities, retaining only instances featuring one of our predefined verbs. After processing, HPDv2 contains 28,783 validated (images, caption, preference) tuples covering 29 verbs, and Pick-a-Pic contains 14,958 across 19 verbs. Each tuple presents two images, with a human annotator selecting the one that best matches the caption. To assess potential human preference biases, we measure how often annotators prefer specific gender or cthnicity representations for given verbs.
We directly analyze preference pairs where the pre- ferred image depicts one gender (e.g., man) and the dispreferred image depicts the other (e.g., woman), thereby capturing explicit gender preference patterns within individual comparison pairs. Figure 3 shows the gender preference bias across 42 verbs in the two datasets. After filtering to ensure valid man-woman pairs, 26 out of 42 verbs in HPDv2 met our criteria. Among these, 69.23% (18/26) showed a preference for men, revealing consistent man- man-preferred ten- dency. In contrast, in Pick-a-Pic, 18 out of 42 verbs qualified, and 61.11% (11/18) showed a preference for women, indicating a relatively woman-preferred trend. Furthermore, Table TO and Figure present the ethnicity preference distributions across the two image reward datascts. Notably, both datasets exhibit strong preference for the White group, 43.34% in HPDv2 and 40.08% in Pick-a-Pic, followed by East Asian and Indian representations. Despite certain verbs showing distinct preferences (c.g., "bake" favor- ing Black individuals and "fish" favoring Fast. Asians),
Foca-R: FBG
20
drnc
Jmm
knnel
cook
Isugh
rood
wash
101
tho
<tate g0up
Stand
Figure 3: Image reward datasets gender preference distribution across 42 verbs.
the overall distributions reveal collected human preferences may implicitly favor Western-centric aesthetics and representation. These imbalances in human preferences might risk propagating representational bias during reward model training, thereby reinforcing existing social inequities in downstream video generation. Collectively, our findings underscore the urgent need for more inclusive and demographically representative preference datasets that capture global diversity.
5.2 Preference in Image Reward Models
Building on our analysis of gender and ethnicity biases in human preference datasets, we next examine how such biases propagate through reward models trained on these datasets.
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Southeoe Middl
M41 M-2 M-5 M- M-5 M-6 M-7
Figure 5: Image examples of our constructed benchmark for evaluating preference in image reward models with generation prompts: "A/An [ethnicity] [gender] is baking [context]]" " We only show the images with [gender] E (man, woman).
Evaluation Benchmark Construction. To systematically evaluate social biases in reward models, we construct a controlled benchmark basod on text-to-image (T2I) generation, inspired by HPDv2 (Wu let al. 2023) and ImageRewardDB (Xu et al.) 2024). Using the event prompting templates introduced in Section 3.2 we employ FLUX [Lass] 2023), a ofate-of-thheatt T21 model, to generate diverse image sets varying systematically across gender, ethnicity, and verb dimensions. The benchmark includes two generation settings: (1) Schnicity+Persor where prompts specify only the actor's ethnicity, and (2) Sthnicity+Gender where both gender and ethnicity are explicitly indicated. Table 3 summarizes prompt coverage and provides representative examples. To ensure statistical robustness, we generate 100 images per prompt, resulting in a large and diverse evaluation set. Sample outputs are illustrated in Figure 5
The use of FLUX is not intended to assume a bias-free image generator, but rather to provide a controlled and repro- ducible setting for probing preference patterns in reward models. By conditioning on systematically varied demo- graphic attributes, our benchmark isolates reward model preferences independently of generator-specific artifacts. To assess benchmark integrity, we conduct human verification study in which three annotators independently reviewed 100 randomly sampled images. While 77% of samples achieved unanimous agreement, this figure is intentionally conservativer a deeper inspection shows that 97% of samples reached ma- jority agreement (at least 2/3 annotators confirming correct attribute representation), with only three images lacking con- sensus. Importantly, these rare disagreements were confined to visually ambiguous prompts rather than systematic failures or demographic-specits distortions. This high signal-tonoise ratio indicates that the visual gender and ethnicity cues are sufficiently clear for reward models to process reliably, and that residual label noise is unlikely to materially affect the observed preference patterns.
Settings # of Prompts
Examples
An East Asian person baking cookies a cony kitchen, with warm light and the aroma of vanilla filling the air.
Ethnicity Person
294
An East Asian woman is cozy
baking cookies kitchen, with warm light and the aroma of vanilla filling the air.
Ethnicity + Gender
1176
Table 3: Statistics of Benchmark Construe- tion Prompts for Image Reward Models. Each prompt explicitly highlights the actor's eth- nicity (when specified), the verbs, and the surrounding context, providing a structured basis for analyzing social representations on image reward models.
Preference Bias Evaluation. We evaluate four image reward models: (1) HPSv2.0 Ww et al, 2023 trained on the HPDv2 dataset; (2) HPSv2.1 (Wu et al. 2023), trained on the unreleased HPDv2.1 dataset; (3) PickScore (Kirstain et al.) 2023b), trained on the Pick-a-Pic dataset; and (4) CLIP (Radford et al.) 2021), which serves as the shared basc model for HPSv2.0, HPSv2.1, and PickScore prior to fine-tuning on their respective preference datasets. Each generated sample is assigned a continuous scalar preference score predicted by the corresponding reward model (e.g., the HPSv2 score), rather than discrete class
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label. These standardized scores are used to quantify the model's relative preference for gendered and ethnic attributes across controlled comparisons. Table f reports two complementary bias metries ethnicity-aware gender bias (PBSg) and ethnic representation distribution (RDS, and SDI). Appendix C provides further
implementation details, a comprehensive analysis across 42 verbs, and a statistical analysis (Table 11
Modols Average
White
Black
Latino
Eass Asian
Southeast
Asian
Iodia
Middle Eastern Overall
PBS
PBSg
RDS
PBS
RDS
PBSa
RDS
PBSc
RDS
PBSG
RDS
PBS.
RDS
PBSg
RDS
SD1
CLIP
0..726
0.0343
0.0182
-0.1198
0.0002
0.0931
-0.0013
-0.1355
0.0141
-0.0655
0.0094
-0.0508
-0.029
-0.0007
40.0108
0.8495
HPS5220
0,0039
0,6090
-0,023
0.7341
-0.0069
0,6512
0.0237
0.4752
000031
0.5192
-0,0100
0,5022
0.0070
0.6164
0.0315
0.8492
+0.0765
+0.5747
-0.0605
+0.8539
-0.001
+0.7416
+0.0250
+0.0067
.0.0172
+0.6057
-0.0191
+0.6430
+0.0369
-0.7071
+0.022
400000
iPSS22.
-0.0984
-0.0533
00.089
0.0257
-0.0321
0000011
0.0382
-0.3044
0.0091
-0.2181
-0.0099
-0.0006
-0.0077
-0.1053
0.0214
0.8470
.0.0258
00.1176
.0.0371
+0.1455
-0.0329
+0.0963
+0.0099
-0.1729
.0.0050
-0.1316
-0.0103
+0.0502
+0.0022
-0.0446
+0.0822
-0.0025
PiekScore
-0.1157
0.0321
0.0089
0.0177
0.0279
-0.3479
-0.0118
-0.2257
0.0316
-0.2163
0.0115
0.1531
-0.0391
-0.1277
-0.027
0.8485
.0.0431
-0.0022
.0.0113
+0.0421
+0.0277
-0.2545
-0.0105
-0.0942
+0000155
-0.1298
+-000221
+0,2039
.0.0092
-0.0070
-0.0163
-0.0012
Table 4: Preference bias of different reward models. All values represent average scores across 42 verbs.
Ethnicity-Aware Gender Bias. We construct preference evaluation prompts in the format "A/An [ethnicity] person is [verb]-ing [context]", " covering all combinations of ethnicity and verb (evaluation: ethnicity+perrson. For each preference prompt, we generate images using generation prompts in the format "A/An [ethnicity] [gender] is [verb]-ing [context]", where gender, ethmicity, and verb are explicitly specified (generation: ethmicityytgener)) The evaluation prompt omits gender to measure reward model's inherent preference, while the generation prompt explicitly specifies gender. The reward scores assigned to these images by a reward model are standardized using their mcan and standard deviation. We then compute the average standardized score across the 100 images for each generation prompl. To compute the final PBSg, we fix the ethnicity and verb, and subtract the average standardized score for women from that for men. Because of the adaptation, PBSg score here can be greater than one. A positive PBSg score indicates a preference for men, while negative score reflects a preference for women. CLIP shows slight woman-preference bias (-0.0726). Fine-tuning on HPDv2 shifts HPSv2.0 toward a strong man-preference (+0.6039), consistent across ethnic groups. In contrast, PickScore ( (..1157) and HPSv2.1 ( (-0.9884) show woman--preference biases, with the latter's training data undisclosed. These shifts aligu with each model's training data, revealing consistent gender preferences across ethnicities. Figures 19 to 25 presents the PBSG scores across 42 verbs for each cthnicity group.
Ethnicity Bias. We use preference evaluation prompts in the form "A person is [verb]-ing [context]" " (evaluation: pors) For each preference prompt, we have generated images using more specific gencration prompts of the form "A/An [ethnicity] person is [verb]-ing [context]", " where the cthnicity and verb are explicitly specified (generation: ethnicity+person). The evaluation prompt omits ethnicity to measure reward model's inherent preference, while the generation prompt explicitly specifies ethnicity. The reward scores for these images provided by a reward model are standardized with mean and standard deviation. We then compute the average standardized score across the 100 images for each generation prompt. To calculate RDS, and SDI, we fix the verb and apply softmax function (Bridle] 1990 Bishop 2006) to normalize the scores for each ethnicity, indicating ethnicity preference within each verb context. A positive RDS, indicates overrepresentation of an ethnicity, while à negative score indicates underrepresentation. A higher SDI score corresponds to more balanced and diverse outputs across all groups. The base model, CLIP, slightly favors White individuals (RDS = 0.0182) and achieves the highest SDI score (0.8495), indicating relatively balanced ethnic representation. After fine-tuning HPSv2.0 shifts toward Middle Eastern (RDS 0.0315), HPSv2.1 loward Latino (RDS- = 0.0382), and PickScore toward East Asian individuals (RDS-
0.0352). All show reduced SD1, indicating decreased ethnic diversity post-alignment. Figures 26 to 29 show the ethnicity bias across 42 verbs.
6 Social Biases in Preference Alignment
Building on our analysis of gender and ethnicity biases in image reward models, we cxamine how preference alignment tuning affects bias in video generation. We fine-tune Video Consistency Model distilled from VideoCrafter--2 (VCM-VC2) (Li et al.) 2024) using three image reward models, HPSv2.0, HPSv2.1, and
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PickScore, and compare social bias distributions before and after tuning to assess how each reward model shapes identity representation. Following the T2V-Turbo-V1 training protocol (Li al. 2024), we incorporate reward feedback into the state-of-the paradigm Latent Consistency Distillation process (Luo et al. 2023) by using single step video generation. During student model distillation from a pretrained teacher text to video model, we directly optimize the decoded video frames to maximize reward scores from the image-texi alignment models, guiding each frame toward representations more aligned with human preferences. Video model post-training and inference details can be found in Appendix G
We evaluate aligned video diffusion models using our evaluation framework ($4) Table 5 reports two metrics: PBSg for gender imbalance across ethnic groups, and RDS, and SDI for ethnicity representation disparity and overall output diversity. Appendix D includes more analysis across 42 verbs and a statistical analysis
(Table 12p
Models
Average
White
Black
Latino
East Asian
Southeast Asian
India
Middle Eastern Overall
PRSc
PRSc
RDS
PRSG
RDS
PRS;
RDS
PRSc
RDS
PRSc
RDS
PRSg
RDS
PRSc
RDS
SDI
VCM-VC2
0.8081
0.7925
0.6405
0.7768
-0.2361
0.8090
0.7115
-0,2333
0.7945
0.8631
0,8071
-0.1690
0.1433
+ HPSS22
0.9116
0.9667
0.3667
0.9211
0.9667
0,8500
0.9214
0,9000
0.8548
-0.3067
0.1257
A
+0.1062
+0.1742
-0,2738
+0.1456
+0.1577
+0.1385
+0.1269
+0.0306
+0.0177
-0.1977
-0.176
+ HPPSv2.1
0.2267
0,1321
0.4286
0.2081
0.3738
0.1571
0.3619
0.1452
0.1786
-0.2266
0.0976
A
.0.7667
-0.6804
.0,2119
-0.5377
-0.4352
-0.5544
-0. 40.4526
-0.7182
-0.6285
.0.2566
-0.0457
+ PickSoore
0.3714
0.3429
0.6833
0.3357
-0.1810
0.7190
-0.1929
0.1450
-0.1952
0.4548
0.2500
0.3515
-0.1143
0.1467
A
-0.4320
-0.4496
10,0428
-0.4401
0.00571
-0.0900
-0.1920
-0.5665
0.0081
-0.3397
-0.6134
-0.4556
10.0547
0.003
Table 5: Social biases of aligned models. All values represent average scores across 42 verbs. Missing values are marked as "_" and indicate cases where the model generated zero recognizable instances of the corresponding ethnicity under the person-only condition (i.e., when ethnicity is not explicitly specified), reflecting a severe lack of diversity that prevents metric computation for those subgroups.
Ethnicity-Aware Gender Bias. We evaluate gender portrayals under the sthnicity+perree condition using the previously defined PBSg metric. A positive PBSg score indicates a lendency to depict men more frequently, while a negative score suggests a preference for women. The base model, VCM-VC2, demonstrates a strong man bias across all ethnicities, which becomes more pronounced with aligmment using HPSv2.0. In contrast, alignment with HPSv2.1 and PickScore significantly reduces PBSg, indicating a shift toward more balanced or woman-preferred outputs. This change reflects the underlying woman bias present in the HPSv2.1 and PickScore reward models, which steer the model away from the man-dominant bias of the base presents the PBSg scores across 42 verbs for each ethnicity group.
model. Figures 30 to
Ethnicity Bias. Under the ethnicity condition, we analyze models' representation balance using the previously defined RDS, and SDI metrics. Positive values indicate overrepresentation, and negative values indicate underrepresentation. Overall demographic balance measured using SDI, where higher values reflect more equitable representation. The base model, VCM- (CM-VC2, strongly favors White individuals (RDS- 0.6405), while Black, East Asian, and Middle Eastern groups are underrepresented. Alignment with HPSv2.1 reduces some disparities by improving balance for White and Black groups, but significantly decreases Latino representation (RDS- -0.4352) and lowers SDI, indicating reduced diversity. In contrast, PickScore achieves the highest SDI and produces more balanced representation across most ethnic groups, resulting in the most demographically equitable outputs. Figure 38 shows the ethnicity bias across 42 verbs.
Temporal Attribute Stability. Table 0 summarizes the temporal consistency of identity portrayals across align models. Overall, alignment substantially improves the technical coherence of video generation. For ethnicity, the mean TAS increases from 0.8904 in the VCM-VC2 baseline to 0.9632 after HPSv2.1 alignment, and the proportion of videos achieving perfect stability rises by 0.2660 (from 0.5670 to 0.8330). Similar gains appear for gender, with stability reaching near saturation at 0.9895 and 0.9440 perfect stability. The standard deviation of TAS also consistently decreases (e.g. -0.6544 for ethnicity), indicating more uniform frame-level consistency across videos. However, this improvement in stability comes with an important caveat. When alignment models inherit biased preferences, the resulting stability can entrench rather than mitigate bias. For instance, HPSv2.0 alignment not only amplifies the overall man nan-preference bias (PBSg rising from 0.8034 to 0.9116) but also locks that bias in temporally-wee with gender stability reaching
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Model
Attribute
Mean TAS
Median TAS
Std TAS
Perfect Stability
VCM-VC2
Ethnicity
0.8904
1.0000
0.1673
0.5670
VCM-VC2
Gender
0.9763
1.0000
0,0829
0,5850
HPPS22
Ethnicity
0.9305.0.000 000000
1.0000
0.1358 0.0a15
0.7050
00.1880
HPSv2
Gender
0.9967
0.0004
1.0000
0.0310
0.0G19
0,9830
00,0080
HPSv2.1
Ethnicity
0.9632
1..r22
1,0000
0.1019
0.0054
0.8330
0.22000
HPSv2.1
Gender
0. 0.0
1,0000
0.0619-n-m1
0.9440--0.00m
PickScore
Ethnicity
0. 00000
1,0000
0.1302
0.7560
PickSoore
Gender
0.9881.0.ora
1.0000
0.0553 0.076
0.9410,a.05m
Table 6: Temporal Attribute Stability (TAS) across modols and attributes. A high TAS score indicates the actor's identity representation is stable and consistent throughout the video. A low TAS score indicates the representation "flickers" or changes, key temporal artifact. Subscripts in red and blue indicate relative improvements and degradations compared to the base model VCM-VC2.
0.9967 and nearly all videos (0.9830) achieving perfect stability. In other words, the model becomes belter al being biased: produces smoother, more coherent, yet more stereotyped portrayals. This finding highlights a critical insight revealed uniquely by our temporal evaluation framework: alignment tuning, while improving the perceptual and temporal quality of generation, deeply embedded in the generative process. VIDEoBIAsEVAL thus not only detects whether a model is biased but also exposes how aligmment can make such bias temporally cealient--taansfoneee artifacts into stable, systematic distortions.
inadvertently make social bias more persistent and
representational
Summary and Implications. Taken together, these findings demonstrate that preference alignment can both mitigate and amplify existing social biascs, depending on the charactoristics of the guiding reward model. While HPSv2.0 reinforces the base model's male- and White-preferred tendencies, HPSv2.1 and PickScore both exhibiting ceman-preferred reward patterns successfully counteract the original man- dominant bias, stcering the aligned model toward more balanced portrayals. However, neither aligmment achieves complete demographic parity, as residual disparities across ethnic groups persist. These observations suggest that the direction and magnitude of social bias in aligned video diffusion models are largely inherited from the bias profile of their reward models.
7 Controllable Preference Modeling for Video Diffusion Models
Building on prior findings, we next examine whether such biases can be made controllable. Specifically, we investigate if adjusting the distribution of social attributes in image preference datasets can systematically steer reward models and consequently video diffusion models toward more equitable or intentionally calibrated portrayals (Sheng et al. 2020). To make this concrete, we take gender how varying gender composition in preference data influences the directional bias and alignment dynamics of the resulting reward and video models.
study, examining
7.1 Image Reward Dataset Construction
We construct two reward datasets: preferred version and prmanrereferred version, using images from to to guide diffusion models toward specef- representations. Each dataset includes 2.94 million preference pairs from the Sthnicity+Gender
set, where cach pair depicts the same action and ethnicity but differs by gender (e.g., M-1 vs. W-1 in Figure 5 Prompts follow the format "A/An [ethnicity] person [action] -ing [context]." In the man preferree dataset, male images are labeled and female images 0; the opposite applics the woman-preferred dataset. To enhance face-free diversity, we also include 537,660 additional image pairs from HPDv2. When applied to a base model with man-prt nan-preference bias, the woman-preferred dataset helps correct this imbalance and promotes more equitable gender representation.
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7.2 Image Reward Model Development & Alignment Tuning
Leveraging the man-preferred and woman-preferred image datasets, we fine-tune two reward models top of pre-trained CL.P vision encoder: the Man-Preferred Reward Model (RMM) and the Preferereeeerred Reward Model (RMw). Each is optimized to reflect gender-specific aesthetic and representational preferences encoded in its respective dataset. As shown in Table 7 RM consistenlly assigns higher PBSg scores across all demographic groups, aligning with man-preferred portrayals, whereas RMw exhibits the opposite tendency, systematically favoring noman-preferred content. This clear divergence demonstrates that reward tuning effectively captures and amplisies gendered preferences. Reward model training and inference delails can be
found in Appendix F
Models
Average
White
Black
Latino
East Asian Southeast Asian
India
Middle Eastern
CLIP -0.0726
0.0343
-0.1198
-0 -0.0934
-0.1315
-0.0865
-0.0508
-0.0607
RMM 1.2800...0
1.4323, 1.552441.6 20.7943.2z7
+1223+1.500
14525-1154
1.619
1.4914,1.55 20.8846.2.4
RMw
-0.7448. 2.63188
-..8279.2.8 -0.6282.2.06
-0.6420.2.10
-0.8042.2.00
Table 7: Preference bias of reward models. All values represent overage scores across 42 verbs.
We intentionally do not train a "fair" or demographically neutral reward model. As discussed in $ the base video generator (e.g., VCM-VC2) already exhibits a pronounced man bias. Under such asymmetric initialization, a neutral reward signal would merely reinforce existing imbalances rather than correct them. To counteract this skew, we employ a deliberately counter-biased reward model specifically, the woman-preferred RMw, analogous to HPSv2.1- which actively steers generation toward gender equilibrium. This targeted alignment strategy yields markedly more balanced portrayals, demonstrating that directional reward tuning can serve as an effective corrective mechanism for bias mitigation.
Building upon these reward models, we further apply RMM and RMw to guide preference alignment. of the base video diffuusion model (VCM-VC2). Using the same preference-optimizaton framework, we obtain two aligned variants: one tuned toward man-preferred and the other toward woman- woman-preferren preferred content. As shown in Table alignment with RMM increases PBSg scores across all demographie groups, reinforcing praeeerfeeeece bias, whereas alignment with RMw substantially reduces these scores, indicating a strong shift toward wonan-preference bias. These results confirm that our controllable preference modeling framework enables fine-gras -heeggrined modulation of gender bias in video generation, offering a principled and flexible means to amplify or mitigate social tendencies in diffusion-based models. Moreover, achieving the most balanced aligned video generator can be framed as a data composition problem: by systematically adjusting the samples in the reward dataset, one can identify an optimal mixture
proportion of man- and woman-preferee that minimizes overall bias while preserving aesthetic fidelity. This insight highlights a promising direction for datasct-driven bias control in alignment tuning. Figures 39 to 43 presents the PBSg scores across 42 actions for each ethnicity group.
Models
Average
White
Black
Latino
East Asian Southeast Asian
India
Middle Eastern
VCM-VC2
0.8034
0.7925
0.7758
0,8690
0.7115
0.7945
0,8634
0.8071
RMa
0.9584.016
0.9595...17
0.952440.18
0.9437 0.9756...11 0.1008
0.9447
0.964044..0
0.9709
RMw
0.3082.a.0
0.334L...6
0.3913.0.38
0.3314..54
0.2639.p.58
0.3446...s2
0.389-La.42
Table 8: Social biases of aligned models. All values represent average scores across 42 verbs.
Summary and Implications. Our controllable preference modeling framework demonstrates that bias in video diffusion models can be systematically directed through the construction and tuning of social-attribute aware reward datascts. By varying gender composition in preference data, we show that reward models learn and Transmit directional biases- reinforcing or counteracting existing tendencies in base generalors. Importantly, introducing counter- biased reward models (e.g., woman-prefenge RMw) can actively correct skewed portrayals, producing more balanced and socially representative generations. Beyond binary control, these results suggest a continuous axis of alignment, where muanced mixtures of man- and noman-preferee data yield tunable trade-offs between bias mitigation and aesthetic coherence. The sensitivity analysis across
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42 actions further reveals that even semantically neutral activities can exhibit large gender shifts under alignment, underscoring the need for fine-grained, event-level auditing. Overall, our findings highlight that dataset composition, rather than architectural intervention alone, offers a powerful and interpretable lever for stecring the social behavior of generative models.
Preteereeddd Post-Tralined Actions Woman Preferred Post Trained Actions
bake
0.58
Cook
shop
PB3 of Allgnmmet Tuning
koad
coughay
$4111
0.66
Die .rin
drink
Acters
Eough slerp
rdo
paint stare malk
0.11
0.58
throw work .reed stretch
stand
1,713
picc
(8111
-0.75
climts shop,
40.
(bugh
-1.00
jift
011 6.
<5
GA
4.25 L50 6.7 1,00 125 A J.EES of Algement turing
Reward Model PB5p
PB5: of Aligmmntt buning
(a) PBSg of video generation model before and after alignment tuning by RMA and RMw. Results are broken down into actions. in man-preferred align- Figure ob and Figure 6c are based on this figure.
(b) Sensitive actions
(c) Sensitive actions in woman- prenan-reeeered align- ment tuning.
ment tuming.
Figure 6: Action-level impact of alignment tuning guided by RMM and RMw.
Which Actions Are Most Sensitive During Alignment Tuning? We investigate how specific actions respond to gender-oriented reward model tuming by measuring changes in PBSg scores before and after alignment. As shown in Figure 6a actions cluster distinctly by the reward model that guides luning: alignment with RMy (teal) amplifies male-preferred portrayals in activities such as erercise, row, and cook, while alignment with RMw (orange) shifts representations toward women-preferred actions like bake, sleep, and sweep. Figure 6b and Figure 6e further quantify these shifts by ranking actions according to their normalized sensitivity (é.e., APBSG divided by each reward model's baseline PBSg). The top-ranked actions sleep, stretch, and read emerge as the most sensitive under both man- and Noman-preferred tuming, revealing that even socially neutral or domestic activities can exhibit pronounced gender bias once alignment is applicd. Together, these results highlight that alignment tuning induces systematic, action-specific bias amplification, and demonstrate the effectiveness of our event-centric evaluation framework in exposing fine- grained behavioral sensitivitics across gendered dimensions.
Responsible Use of Controllable Preference Modeling. While results demonstrate that controllable preference modeling can effectively steer social attribute distributions, emphasize that technical "debiasing" is not a silver bullet and carries significant risks if applied unilaterally. This technique should be applied thoughtfully as a tool to support, rather than replace, diverse stakeholder input. Defining "equitable representation" is fundamentally a context-dependent normative decision that requires active community consultation. We encourage practitioners to engage with the communities impacted by these systems to determine appropriate representation goals, rather than relying solely on automated optimization to resolve complex social biases.
8 Conclusion
In summary, our work exposes and investigates key blind spots in evaluating social bias within lext-to-video generation. Through the introduction of VIDEOBIAsEVAL, we establish a comprehensive framework that decouples identity attributes from content semantics and systematically tracks how alignment tuning reshapes social representations. Our analyses demonstrate that soward-model-baee based alignment not only inherits but frequently amplifies existing biases encoded in human preference data. These findings highlight the importance
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of integrating bias anditing and mitigation throughout every stage of the video generation pipeline, advancing the development of more equitable and socially aware generative systems.
Limitations
While our work presents comprehensive evaluation of social biases introduced through alignment tuning in video diffusion models, several limitations warrant further consideration. First, our analysis focuses on two social dimensions, gender and ethnicity, using predefined categories based on U.S. Census conventions and prior literature. These categories, while practical for controlled evaluation, are inherently socially constructed and cannot fully capture the fluidity, interscctionality, or cultural muances of identity (iin et al. (2024): : Qiu et al. (2024a 2025). Future work should explore richer identity representations, including intersectional groups. Second, our VL.M-based evaluators, though validated against human judgments, rely image-level classification and may exhibit their own biases or inaccuracies, particularly when interpreting identity in stylized or ambiguous frames. While we ensemble multiple models to mitigate this, ground truth annotations for a larger and more diverse set of videos would further strengthen the reliability of our measurements. Third, we primarily assess alignment impacts under a specific training strategy (singlo-step latent consistency distillation) and a limited set of reward models. Other training protocols, such as RL-based luning or multi-turn video instruction alignment, may exhibit different bias dynamics not captured in our study. Fourth, our controllable preference modcling experiments, while demonstrating the fcasibility of targeted bias modulation, constrained to synthetic manipulations of gender preference. These interventions do not address broader questions of value aligmment, normative appropriateness, are crucial for the responsible deployment of generative video systems. Lastly, our evaluation framework, ViDEoBiAsEVAL, is currently benchmarked on a fixed set of 42 socially associated actions. While this enables fine-grained control, it may limit generalizability to open-ended generation settings or novel actions not. covered in our taxonomy. We hope that these limitations encourage further rescarch into holistic, culturally grounded, and ethically aligned evaluation pipelines for video generative models.
long-term societal impact, which
Broader Impact Statement
As video diffusion models transition from research prototypes to widely deployed tools for content creation, their influence visual culture and socictal representation becomes incrcasingly significant. Our work highlights a critical but often overlooked consequence of this adoption: the role of aligument tuning in stabilizing and entrenching social biases.
Stabilization of Bias. While alignment tuning (c.g., via RLHF) is essential for improving the visual fidclity and temporal consistency of generated videos, our findings reveal that it can inadvertently act as a "bias stabilizer." By removing visual artifacts and smoothing temporal flickers, alignment tuning makes stereotyped representations appear more natural, coherent, and authoritative. Unlike the obvious errors in unaligned models, these "polished" biases are harder to detect as failures, risking the subtle normalization of skewed demographic portrayals in synthetic media. Tf left unchecked, this stabilization could reinforce harmful societal stereotypes under the guise of high-quality, preferred content.
Dual-Use of Controllable Preference Modeling. Furthermore, our exploration of controllable preference modeling 7 underscores the dual-use nature of these techniques. We demonstrate that by curating preference datasets, reward models can be engineered to systematically amplify or suppress specific social attributes. While this capability offers powerful mechanism for correcting historical underrepresentation and mitigating bias, it fundamentally renders the alignment process double-edged sword. The same tools used to promote fairness could be exploited to intentionally exclude specific groups, enforce homogenization, or generate propaganda that aligns with exclusionary worldviews.
Mitigation and Responsibility. These risks necessitate a shift in how we evaluate generative video systems. Relying solely on aesthetic metrics is insufficient; developers must adopt multi-granular, event-centric auditing frameworks like VIDEOBIASEVAL-- continually monitor the social composition of generated content. We urge the research community and practitioners to treat preference dataset curation with the same rigor
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model architecture design, ensuring that the "human preferences" guiding these systems reflect diverse and equitable society rather than reinforcing existing prejudices.
Acknowledgment
We thank the anonymous reviewers for their thoughtful feodback and suggestions. This research was supported in part by the AFOSR MURI grant #FA9550-22 1-0300 the Office of Naval Research (ONR) under Award #N00014-23-1-2750, and the National Science Foundation under Award 24487-4 Additional support was provided through cloud credits from the NVIDIA Academic Grant Program and funding from the Technical AI Safety Research Program at Coefficient Giving. The views and conclusions expressed in this work are those of the authors and should not be interpreted as representing the official policies endorsements of AFOSR, ONR, the U.S. Government, the National Science Foundation, NVIDIA, or Coefficient Giving.
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A Social Biases in Video Generative Models
We apply our proposed evaluation framework to four state-of-the-art video diffusion models with varying alignment strategies. In this work, use the term aligned models to refer to video diffusion models that undergo post-training optimization to better match human preferences, typically via reward-weighted fine-tuning or RLHF-style procedures using learned preference or aesthetic reward models. Concretely, this alignment process follows a general pipeline of base video diffusion model + cefference/rewaatte model - post- training optimization ion. Under this definition, the aligned models include Instruct Video (Yuan el al.] 2024). which builds on ModelScope (Wang et al.) 2023a) and is aligned using HPSv2.0, as well as T2V-Turbo V1 (Li ct al. 2024). which builds on VideoCrafter-2 (Chen et al. 2024a) and is aligned with HPSv2.1, InternVid2-S2 (Wang al. 2024b), and ViCLIP (Wang et al. (023b). Their unaligned counterparts, ModelScope and VideoCrafter-2, serve as baselines for controlled comparisons, allowing us to isolate the effects of alignment. from those of base model architecture.
For implementation, we use the official code repositories provided by the respective papers and run inference on 1 to NVIDIA A100 80GB GPUs. To compute the social bias distribution, as outlined in $ we generate videos with each prompt 10 times per model with different random seeds and average the results to reduce sampling variance. Table 2 reports two social bias metrics: ethnicity-aware gender bias (PBSg) and ethnic representation distribution (RDS, and SDI).
Ethnicity-Aware Gender Bias. We evaluate gender portrayals under the ethnici temniicitytperra condition using the PBSg metric. Our analysis reveals a consistent male bias, with average PBSg values remaining above zero across all ethnic groups. Notably, alignment tuning appears to amplify this imbalance. Instruct Video and T2V-TurboVVi exhibit PBSg increases of 0.04 and 0.0725, respectively, indicating that preference-basck fine-tuning may worsen gender disparity rather than alleviate it. Figures to 13 present the detailed PBSg scores across 42 verbs for each ethnicity.
Ethnicity Bias. Under the person-only condition, we analyze models' reprosentation balance using the previously defined RDS, and SDI metrics. All models show a pronounced overrepresentation of White individuals, though the magnitude varies. ModelScope exhibits the strongest imbalance (RDSwhite = 0.769, SDI = 0.0538), which is further amplified by alignment tuning in Instruct (RDSwaite = 0.783, SDI = 0.0267). V achieves moderately improved balance (RDSwhite = 0.6905, SDI = 0. 0.1252), while T2V-Turbo-V V1 further reduces White dominance (RDSwnite 0.6381) but at the cost of lower diversity (SDI 0.1119). Overall, while alignment tuning may alleviate certain ethnic skews, it can also suppress demographic diversity, suggesting trade-off between bias reduction and representational variely. Figure 14 show the ethnicity bias across 42 verbs.
How Docs the WebVid-10M Distribution Shape Bascline Model Behavior? To interpret the source of baseline model behaviors, we analyzed the demographic composition of the WebVid-10M (Bain) eet al.) 2021) training dataset. By filtering the dataset for our 42 target verbs, we extracted a subset of 469,153 videos containing gender attributes and 67,081 videos containing ethnicity attributes. Our analysis reveals a distinct misalignment regarding gender: while the training data exhibits slight female skew (mean PBSg of -0.,,0), the unaligned base models demonstrate a significant male bias (PBSg of +0.4815 for ModelScope and +0.7581 for Video-Crafter-22) This indicates that the strong "Male Default" " in baseline models does not stem from the WebVid-10M verb distribution, but likely originates from biases inherent in the underlying Text-to-Image backbone during pre-training. Conversely, ethnicity biases in the base models mirror the training data's distribution which skews towards White (RDSwhite of +0.2489) and Southcast. Asian identities but exaggerate the magnitude. These findings clarify that while pre-training establishes biased baseline (sometimes contradictory to the video training data), our study specifically isolates how subsequent alignment tuning propagates or amplifies these pre-existing states. Figure 15 and Figure 16 visualize these distributions across all 42 verbs.
Statistical Analysis of T2V Models. We perform paired statistical significance tests on aligument-indiee induced differences (A) between each base model and its aligned counterpart, with results summarized in Table g For each metric, we compute A = Paligned - Pbase: where positive values indicate an increase after alignment. Statistical reliability is assessed using the p-value, which measures the probability of observing difference
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ModelScope - InstructVideo
VideoCrafter V2 - T2V-Turbo-Vi
Metric
Base
Aligned
A
p-value
EII. Size
Sig.
Base
Aligned
p-value
Eit. Size
Sig.
Bllnniiity-- Antar: Gender Hiaa: Proportion Mlas Soowe for Gender (P88c)
Average
0.181
0.529
+0.045
0.1079
-0.254
Small Med
No
0.758
0,831
+0.072
0.0089
-0.424
Small Med
Yes"*
White
0.568
0.568
-0.010
0.8261
0.004
Small
No
0.749
0.871
+00125
0.0027
-0.4553
Small-Med
Yes**
Black
0.391
0.511
+0.120
-0.487
Small Med
Yeu**
0.617
+0.193
Med- Med-Large
Yest
Indian
0.394
0.488
+0.004
0.0306
-0.346
Mied
You*
0.803
0.776
-0.027
0.5097
0.082
Small
No
East Asian
0.441
0.428
-0.012
0.8373
0.032
Small
No
0.038
0.776
10.079
0.0920
-0.206
Small-Med
No
Southeast Asian
0.461
0.502
+0.041
0.3967
00132
Smail
0.527
+0.006
Small Med
No
Middle Eastern
0.463
0.539
+0.056
0.2304
-0.188
Smoll
No
0.756
0.766
+-00011
0,7669
-0.046
Small
No
Latino
0.631
0.673
0.042
0.3637
-0.146
Small
No
0.860
0.921
1 10.002
0.0073
-0.496
Small-Med
Yes**
Ethoicity Bios: Repreacntation Deviotion Score for Ethosesty (RDS.)
Black
-0:195
-0.198
-0.002
0.088
Small
East Aslan
-0.181
-0.199
-0.017
0.0831
0.340
Small-Mod
Yes*
-0.150
-0.326
-0.176
0.0000
1.105
Largo
Yostnn
Latino
-0.195
-0.193
+0.002
0,5700
-0.088
Small
No
Middle Eastern
-0.198
-0.195
0.000
0.0602
-0.068
Small
No
-0.150
-0.229
-0.079
0.0049
0.459
Small-Med Med Large
Yesh
White
0.789
0.783
+0.014
0,2139
-.182
Small
No
0.686
0.355
-0.31
0.0001
0.092
Yest
Ethnicity Blus: Sizepoon's a Diveraily Judex: (SDI)
Overall
0.054
0.027
-0.027
0.1710
0.215
Smali-Med
0.131
0.109
-0.021
0.4920
0.107
Small
No
Table 9: Statistical comparison of base and aligned text-to-video diffusion models under paired evaluation. We report mean metric values for each base model and its aligned counterpart, along with the alignment-induced difference A Maligned - Mbase- Statistical significance is assessed using paired tests across the same set of verbs, with p-values indicating the reliability of observed differences and Cohen's 's d quantifying standardized effect size. Significance levels are denoted as *** p < 0.001, "pp < 0.01, and "p < 0.05. Effect sizes are categorized as Small (id) < 0.2), Small Medium (0.2 S [d| < 0.5), Medium Large (0.5 < |d] < 0.8), and Large (ld) 2 0.8). PBSg measures ethnicity-aware gender bias, RDS, captures deviation from uniform ethnic representation, and SDT reflects overall ethnic diversity (higher more balanced). Missing entries (-) indicate cases where metrics are undefined due to zero observable instances for the corresponding subgroup.
at least as large as A under the mull hypothesis of no alignment effect (with p < 0.05 indicating statistical significance), while practical magnitude is quantified using Cohen's d, which captures the standardized effect size independent of sample size (with larger |d] indicating more substantial changes] Q Interpreting these measures jointly allows us to distinguish statistically reliable effects from practically meaningful ones. For ModelScope + Instruct Video (HPSv2.0 aligmment), only 3/14 metrics exhibit statistically significant changes (p < 0.05), all with small-to-modium effect sizes, indicating localized rather than global bias shifts: PBSg increases for Black (A = +0.120, p 0.0030, d = 0.49) and Indian (A +0.094, p = 0.0306, groups, while East Asian representation shows modest decrease in RDS, (A -0.017, p 0.0331, d 0.34), with no significant change in overall ethnic diversity as measured by SDI. In contrast, VideoCrafter-V V2 V1 (HPSv2.1 1 alignment) yields broader and stronger effects, with 7/12 metries reaching statistical significance and several exhibiting medium-to-aarge or large effect sizes, including a substantial decrcase in East Asian RDS, (A = -0.176, p < 0.001, = 1.11) and a marked increase in Black PBSg (A = +0.199,, p p= 0.0001, = 0.68), a alongside significant shifts in White representation and gender preference (RDS.: A = -0.131, p- = 0.0001, d 0.69; PBS;: A +0.123, p = 0.0027, d = 0.49), again without corresponding improvements in SDI. Overall, these results show that alignment tuning induces statistically reliable and sometimes large redistributions in gender and ethnic representation, with the scope and magnitude of these shifts strongly dependent on the reward model, and that statistically significant changes do not, necessarily imply uniform debiasing but often reflect bias reallocation across demographic groups.
0.35)
+
2 Pinterpreting statistical tests. The p-value quantifies the probability of observing difference at least as large as the measured A under the null hypothesis of difference between models. We adopt standard significance thresholds; p < 0.001 (***, extremely strong evidence), p 0.01 (**, strong evidence), p < 0.05 (*, moderate evidence), and p 2 0.05 (insufficient evidence to reject the null). Complementarily. Cohen's d mcasures the standardized effect size, capturing the practical magnitude of the difference independent of sample size, with conventional interpretations of [d] < 0.2 (small), 0.2 S |d] < 0.5 (small mediumm), 0.5 < [d] < 0.8 (medium-largee and [d] 0.8 (large).
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PeckSoap der Neura (9S5
rcod
pice
blle
tirk
shout
jump
cat
exendse
Muddy
sandd
GRu by
pry
pick
1156
4.5
Rabe
2000
clean
4.22 4.5
Faz 7.5
SwAp
stare
cough
10ck
piten
slep
climb
wort
2012
work
512
cook
cook
pftch
oough
jamp
coe
rish
alt taugh
(a) ModelScope (gray) vs. Instruct (orange)
(b) Tideo-Crafter- V2 (gray) vs. T2V-Turbo- V1 (teal)
Figure 7: Ethnicity-aware gender bias for the White subgroup across 42 verbs. We compare unaligned and aligned model pairs: ModelScope (unaligned) vs. Instruct (aligned), and VideoCrafter-V2 (unaligned) vs. T2V-Turbo-V (aligned). Values are plotted relative to a neutral zone centered at zero; regions outside this zone indicate systematic bias, with positive values denoting man-proference (+) and negative values demoting woman-preference (-).
Mode Suape remuctiios
Crass
TEWtuite/s
(PS5-4)
walk stand
shout
kick
paint
drink
sloep
work
exerdse
study
cot
paint
read
Sweep
shop
drive
2hop
60
6.75
babe
shout
755
pray
stare
drink
cry
tough
phch
Ineel
work
c9mb
1augh
fish
Ift
Jmp
wich
ride
Fish
ocan throw
cook
clim drive
(a) ModelScope (gray) vs. Instruct Video (orange)
(b) Video-Crafter-V (gray) vs. T2V-Tubb-VV V1 (teal)
Figure 8: Ethnicity-aware gender bias for the Black subgroup across 42 verbs. We compare unaligned and aligned model pairs: ModelScope (unaligned) InstructVideo (aligned), and VidcoCrafter-Vy (unaligned) vs. T2V-Turbo-VI (aligned). Values are plotted relative to neutral zone centered at zero; regions oudside this zone indicate systematic bias, with positive values denoting man mreferefeee (+) and negative values denoting woman- voman-preference (-). (-).
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Crater Meuro (9PS--2
cook
more malt
rOW
Mauraa (96--0)
pick
pray
Bile
wort
play
rood
d11k
pick
drink
throw
shop
KIck
15h
53KO
20000
study
setch
swap
CHerese
Shout
stand
throw
jump
clmb
fish
cookcouugh
epparcise
(a) ModelScope (gray) vs. Instruct (orange)
(b) Video-Crafter- V2 (gray) vs. T2V-TubboVVL (teal)
Figure Ethnicity-aware gender bias for the East Asian subgroup across 42 verbs. We compare unaligned and aligned model pairs: ModelScope (unaligned) InstructVideo (aligned), and VideoCrafter-V (unaligned) T2V-Turbo-V V1 (aligned). Values are plotted relative to neutral zone centered at zero; regions outside this zone indicate systematic bias, with positive values denoting man- man-preference (+) and negative values denoting woman-prestn -rmann-reference (-).
ModdSesss
heubral (rep-2)
read
clear
pick
pray
dimb
SWOOD
ride
walk
pr
deop
babe
TOW
Weop
sleep
25
Mhop
ax Riss 9.50
2.25 9.5
bako
r0tch
MOFE
pttch
cook
work
cough
bize
Glmb
drnk
w3lk
Urow
shout
laugh
fish
cough
jump
5mile
kick
know
piuh
shout
(a) ModelScope (gray) vs. Instruct (orange)
(b) Video-Crafter-V2 (gray) vs. T2V-Turbo-Vi (teal)
Figure 10: Ethnicity-aware gender bias for the Southeast Asian subgroup across 42 verbs. We compare unaligned and aligned model pairs: ModelScope (unaligned) vs. Instruct Video (aligned), and VideoCrafter-VV (unaligned) vs. T2V-Turbo-V V1 (aligned). Values are plotted relative a neutral zone centered at zero; regions outside this zone indicate systematic bias, with positive values denoting man-preference (+) and negative values denoting woman-preference (-).
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Mianfrare atstee
cook
ride
paint
TOW
SToP
Keara (PS5-4)
call
Halk
plck
snile
pra
pick
paint
pluh
cry
bite
cllbb
stand
cough
w0dh
thow
14t
76h
200
wach
dimb
Jump
drive
cough
kncd
smile
staty
work
read
shout
(a) ModelScope (gray) vs. Instruct (orange)
(b) Video-Crafter--V (gray) vs. T2V-Turbo-V V1 (teal)
Figure 11: Ethnicity-aware gender bias for the Indian subgroup across 42 verbs. We compare unaligned and aligned model pairs: ModelScope (unaligned) vs. Instruct (aligned), and VideoCrafter-V2 (unaligned) vs. T2V-Tubbo-V (aligned). Values are plotted relative to neutral zone centered at zero; regions outside this zone indicate systematic bias, with positive values denoting man- man-preference (+) and negative values denoting woman-preference (-).
Medelicape intrumsttee paint
clean walk ride
pick
Meubil (T85-0))
exendse
drink
cry
drive
Maurral (PSS-3)
Mick
cry
pick
play
Swoop
IIt
read
babs
shop
sleep
cook
read
store
bibw
shop
shand
study
drve
<lmb
work
knoel
5loc0
cough
7un
5k370
thoo
jump
teigh
<hate
cough drik
fusl leagg
pith
ride
shout
(a) ModelScope (gray) vs. InstructVideo (orange)
(b) Video-Crafter-V2 (gray) vs. T2V-Turbo-V V1 (teal)
Figure 12: Ethnicity-aware gender bias for the Latino subgroup across verbs. We compare unaligned and aligned model pairs: ModelScope (unaligned) vs. Instruct (aligned), and VideoCrafter-V (unaligned) D2V-Turbo-Vi (aligned). Values are plotted relative to neutral zone centered at zero; regions outside this zone indicate systematic bias, with positive values denoting man-preference (+) and negative values denoting woman-preference (-).
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VacelScape instuctiided
V Video Crefee
T24
Halk parferrise
cal
2000
Mand
Shous shop
sleep
(185
Mick
shout
exendise
tike
pray
stand
read
1C33
paint
cough
1425
R.50
pray
CHl
drink
sacc
6.25
C77
4.75
OST
038C
pitch
wash
5ite
111
work
throw
dlaa
study
buuh
clmb
smle
Cook
1ish
dnnt
nun
dlimb
10W
kned
jump
throw
stabe
Jamp kneel lough
pick
(a) ModelScope (gray) vs. InstructViden (orange)
(b) Video-Crafter--2 (gray) vs. T2V-Turbo-Vi (teal)
Figure 13: Ethnicity-aware gender bias for the Middle Eastern subgroup across 42 verbs. We compare unaligned and aligned model pairs: ModelSeope (unaligned) vs. Instruct Video (aligned), and VideoCrafter--2 (unaligned) vs. T2V-Turbo- V1 (aligned). Values are plotted relative to a neutral zone centered at zero; regions outside this zone indicate systematic bias, with positive values denoting man-preference (+) and negative values denoting woman-preference (-).
Model5cope
Percetlgge
Ethnicity
black
instructVideo
Indian
asian
Percettoge
1.0
latino
05
white
8.
pairi
ride
Jlep
word
port mort
Cddoo-Cratter22
Percertag
Ethnicit
black
T2V.uuba-VV
Perentagg
1.0
latino
middle caste
bale
cal
dear
como
cosk
drive
Me
Jump
knowl
pray
read
vide
stop
phout
alote
Kerd
durd
south
more
Figure 14: Ethnicity bias distribution across different unaligned and aligned model pairs: ModelScope (unaligned) vs. Instruct Video (aligned), and VideoCrafter-V -2 (unaligned) vs. T2V-Turbo-V V1 (aligned).
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Gender Distribttion (Ni to Rop 42 ont
Zand
(806
0096
Peroeraaee
502
500
Figure 15: Gender distribution across 42 verbs in the WebVid-10M training data. Top: raw gender frequency distribution. Bottom: relative gender distribution measured by proportional bias score (PBS), highlighting deviations from balanced baseline.
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Brinket ceanbutor (52 Top Eiterity
Fas Adlan
Ethnicity RDS by Action (Top 30 Actions)
RDS Over-represented RDS
Ideal uniform RDS < 0: Under-represented
0.5
0302
5mil
3:43
0.44
0.14
0.04
0.181
0:11
104
Marz
1027
0348
0303
0124
0352
0.08
0326
4.66
010
8.56
4316
3238
3.040
0323
0322
0.14
0.544
Med
4.003
4.388
0.043
4.10
4.05
3.64
0246
0.55
lugh
2.0
0.33
4.04
0.36
4.538
4308
0.009
4321
3385
0.10
KOX- MAN Scorl
PPay
000k
0.166
0.466
0.007
0.59
0.44
0281
0.000
Ohv
41552
4154
01593
0105
4188
0188
0151
clerr
4.52
9161
0355
4311
9.68
226
slep
4,230
3.35
0324
0.22
4014
3.35
0776
Srook
0.35
4.33
4311
1.000
0.22
0.085
0.00
2.2
stady
0.005
4.54
0.08
0.138
4.084
3.50
0.16
0.24
0.11
0.13
4.14
8.600
4.65
4.57
3.300
4.838
souge
2.45
2515
0.24
4312
0141
Back
Masin Evetre
Bast
Sostesst
Ehnkkt
Figure 16: Ethnicity distribution across 42 verbs in the WebVid-10M training data. Top: raw gender frequency distribution. Bottom: relative gender distribution measured by Representation Deviation Score (RDS), highlighting deviations from a balanced baseline.
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B Social Biases in Image Reward Datasets
We analyze two widely used image reward datasets investigate human preference biases: HPDv2 (Wu et al. 2023 and Pick-a-Pic (Kirstain ef al. 2023b). For each dataset, we extract gender, ethnicity, and verb attributes from image captions using GPT-4o-mini, and classify attributes from images using three VLMs (Qwen2-VL-71) Qwen2.5-VL-7B, InternVL2.5-8B), We then aggregate the social attributes from both caption and image modalities, retaining only instances featuring one of our predefined verbs. After processing, HPDv2 contains 28,78% 783 validated (images, caption, preference) tuples covering 29 verbs, and Pick-a-Pic contains 14,958 across 19 verbs. Each tuple presents two images, with a human annotator selecting the one that best matches the caption. To assess potential human preference biases, we measure how often annolators prefer specific gender or ethnicity representations for given verbs.
PB5
fiaeercise
eat
drive
drink
Neutral
HPD.v2 PBS
jump
cough
kneel
cook
laggh
climb
clean
paint
0.5c
2.75
call
100
pick
bike
9:75
pitch
bake
work
read
wash
ride
row
throw
sweep
shop
study
shout
stretch
skate sleep
smile stand
Figure 17: Image reward datasets gender preference distribution.
We directly analyze preference pairs where the preferred image depicts one gender (e.g., a man) and the dispreferred image depicts the other (e.g., a woman), thereby capturing explicit gender preference patterns within individual comparison pairs. Figure [7 shows the gender preference bias across 42 verbs in the two datasets. After filtering to ensure valid man womaw pairs, 26 out of 42 verbs in HPDv2 met criteria. Among these, 69.23% (18/26) showed a preference for men, revealing a consistent man-preferred tendency. In contrast, in Pick-a-Pic, 18 out of 42 verbs qualified, and 61.11% (11/18) showed a preference for women, indicating a relatively woman-preferred trend. Furthermore, Table IO and ?? present the ethnicity preference distributions across the two image reward datasets. Notably, both datasets exhibit a strong preference for the White group, 43.34% in HPDv2 and 40.08% Pick-a-Pic, followed by East Asian and Indian representations. Despite certain verbs showing distinct preferences (e.g., "bake" favoring Black individuals and "fish" favoring East Asians), the overall distributions reveal collected human preferences may implicitly favor Western-centric aesthetics and representation. These imbalances in human preferences might risk propagating representational bias during reward model training, thereby reinforcing existing social inequities in downstream video generation. Collectively, our findings underscore the urgent need for more inclusive and demographically representative preference datasets that capture global diversity.
Table 10 presents the ethnicity preference distribution across the two image reward datasets, while Figure 4 provides a fine-grained breakdown across 42 verbs. Notably, both datasets exhibit. strong preference for the
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White group, 43.34% in HPDv2 and 40.08% in Pick-a-Pic, followed by East Asian and Indian representations. Despite certain verbs showing distinct preferences (e.g. "bake" favoring Black individuals and "fish" favoring East Asians), the overall distributions reveal a pronounced imbalance skewed toward White representations. This suggests that the reward signals used to guide image generation may reflect and reinforce ethnic biases embedded the datasets. This imbalance in collected preferences risks might propagate representational bias during reward model training, ultimately reinforcing socictal incquities downstream video generation. These findings underscore the urgent need for more inclusive and representative datasets that reflect global demographic diversity in both identity and activity contexts.
East Asian
Southeast Asian
Middle Eastern
Datasets
White Black Latino
India
HPDv2
43.34
9.16
4.44
19.38
1.39
20.20
2.09
Pick-a-Pic
40.08
15.36
8.51
19.94
0.20
13.34
2.56
Table 10: Ethnicity distribution across reward datasets (in %).
P-ck-
1.00
0.50
0.25
Ethricity
0.00
black
east asian
india
1.00
middle eastem
0.75
southeast asian
0.00
bake bike
call
cloan
cimb
cook
cough
cry
wrikk
out
hit
jump
kick
Inse
taugh
lit
paire
pick
pitch
pray
retar
ride
che chose
ctand aate
stracts
couyy
sreup
andk
wonn work
Actions
Figure 18: Ethnicity preference distribution across 42 verbs.
C Social Biases in Image Reward Models
Preference Bias Evaluation. We evaluate four image reward models: (1) HPSv2.0 (Wu el al. 2023 trained on the HPDv2 dataset; (2) HPSv2.1 (Wu et al.) (023), trained on the unreleased HPDv2.1 dataset; (3) PickScore (Kirstain et al.) 2023b trained the Pick-a-Pic dataset; and (4) CLIP (Radford et al. 2021), which serves the shared base model for HPSv2.0, HPSv2.1, and PickScore prior to fine-tuning on their respective preference datasets. Each generated sample is assigned continuous scalar preference score predicted by the corresponding reward model (e.g., the HPSv2 score), rather than a discrete class label. These standardized scores are used to quantify the model's relative preference for gendered and ethnic attributes across controlled comparisons. Table | reports two complementary bias metrics metrics-ethhnoity-eee ethnicity-aware gender bias (PBSg) and ethnic representation distribution (RDS, and SDT).
Ethnicity-Aware Gender Bias. We construct preference evaluation prompts in the format " "A/An [ethnicity] person is [verb]-ing [context]", covering all combinations of ethnicity and verb (evaluation: ethnicity++prron)) For cach preference prompt, we generate images using generation prompts in the format "A/An [ethnicity] [gender] is [verb]-ing [context]]" ", where gender, ethnicity, and verb are explicilly The evaluation prompt omits gender to measure reward model's
specified (generation: ethnicity+gender" inherent preference, while the generation prompt explicitly specifies gender. The reward scores assigned to these images by a reward model are standardized using their mean and standard deviation. We then compute the average standardized score across the 100 images for each generation prompt. To compute
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the final PBSc, we fix the ethnicity and verb, and subtract the average standardized score for women from that for men. Because of the adaptation, PBSg score here can be greater than one. A positive PBSg score indicates a preference for men, while a negative score reflects a preference for women. CLIP shows slight woman-preference bias (-0.0726). Fine-tuning on HPDv2 shifts HPSv2.0 toward a strong man- man-preference (+0.6039), consistent across ethnic groups. In contrast, PickScore ( (-0.1157) and HPSv2.1 (-0.0984) show woman-preference biases, with the latter's training data undisclosed. These shifts align with each model's training data, revealing consistent gender preferences across ethnicities. Figures 19 to 25 presents the PBSg scores acToss 42 verbs for each ethnicity group.
Ethnicity Bias. We use preference evaluation prompts in the form "A person is [verb]-ing [context]" " (evaluation: person-only). For each preference prompt, we have generated images using more specific generation prompts of the form "A/An [ethnicity] person is [verd]-ing [context]", where the ethnicity and verb are explicitly specified (generation: ethnicity+person). The evaluation prompt omits ethnicity to mcasure reward model's inherent preference, while the generation prompt explicitly specifies othnicity. The reward scores for these images provided by a reward model are standardized with mean and standard deviation. We then compute the average standardized score across the 100 images for each generation prompt. To calculate RDS, and SDI, we fix the verb and apply softmax function (Bridle 1990 Bishop 2006) to normalize the scores for each ethmicity, indicating ethnicity preference within each verb context. A positive RDS, indicates overrepresentation of an ethnicity, while a negative score indicates underrepresentation. A higher SDI score corresponds to more balanced and diverse outputs across all groups. The base model, CLIP, slightly favors White individuals (RDS- = 0.0182) and achieves the highest SDI score (0.8495), indicating relatively balanced ethnic representation. After fine-tuning, HPSv2.0 shifts toward Middle Eastern (RDS
= 0.0315), HPSv2.1 toward Latino (RDS = 0.0382), and PickScore toward East Asian individuals (RDS
0.0352). All show reduced SDI, indicating deccrased ethnic diversity post-alignment. Figures 26 to 29
show
the ethnicity bias across 42 verbs.
CLIP
-IPS
+ +IPSV2.1
-PeckScore
Metric
Hasw
Muun
p-val
d
EXI
Sig.
Moan
A
yeval
EII.
Sig.
Munn
p-val
Eir.
Sip.
Albnicitg-Aso Gender Biov: Proportiun Blor Soun for Grader (P8Sp)
Average
.0053
+9.675
11.50
You
.0098
-0.225
0.621
0.38
No
.0.136
00.443
0.412
0:10
White
0.34
0.000
+0337
0.000
-1:88
L
Yostss
-0083
-0118
0.047
0.32
S-M
Yes*
0.032
-0.000
0.176
0.01
S
No
Black
-0.120
0.784
+0.854
0.000
-1.54
L
Yortee
0.025
+0.145
0.025
-0.35
S-M
Yes"
-0.078
40.042
0.422
-0.38
S
No
Indlan
.0.51
0,202
+0.643
0,000
-1.33
L
Youtee
-0.001
+0.020
0.445
00.12
S
No
0.153
+0.204
0,006
-0.44
S:34
Yos*
East Astan
-0.332
0.475
+0.007
0,000
-1.31
L
You
-0:30
-0175
0.10
0.42
S-M
Yes*
-0.226
-0,094
0.113
0:25
S-M
No
Sout
-1.48
You
0:51
S-M
Yes*
9-M
Yoe
Middle Easteru
.0001
0.646
+0.707
-1.54
-0.105
-0.43
0.403
0.13
No
-0.128
0.367
0.277
0.17
No
Latino
0.003
0.651
+0.745
0,000
-1.71
L
You
00003
+0.050
0.125
0.24
S-M
No
0.348
0.255
0.000
0.79
M.L
YosHA
Klhninity Biux Reprosenaaiion Dacistiin Stone for Ruhnicity (RJ88.)
Black
0.000
-0.008
-0.008
0.173
0.22
S-M
No.
-0.051
-0.631
0.093
0.96
I.
Yie
0.028
+00-22
0.000
-0.72
M-L
You
East Astan
0.133
-0.017
0.53
M-L
Yos*
0,000)
-0.004
0.499
0.11
S
No
0.002
-0.33
M-L
You
-0.008
-0:98
L.
You
-0007
-0.56
M-1.
Yee*-
-0.009
0.022
0:38
S-M
You
Todino
-0.001
0.025
+0005
0.000
-0.71
M-L
0.858
+0.030
0.000
-1.00
1.
-0.011
-0.010
0.053
0:32
S-M
Middle Enstaru
-0.010
0.002
+0.042
0.000
-1.52
L
You
0.023
+0.033
0.000
-1.18
L
Yis
-0.025
-0.006
0.002
0.54
M-L
Your
Southeast Astan
0.08
40.010
-0.018
0.001
0.00
M-L
You
-0009
-1018
0001
0.56
M-L
Yee
0.011
+0.00
0.811
-0.08
8
No
White
0.17
-0.013
-0.050
0.000
1.27
1.
Yeu
-0.020
-0.07
0.000
0.51
M-T.
Yoe
0.005
-0.012
0.138
0.21
8-M
No
Altnnictty Biax: Siagoon's Disersity
(SD11
Overall
0.800
-0.001
0.607
0.07
No
0.847
.0.003
0.095
0.27
S-M
No
0.548
-0.001
0.877
0.14
No
SwSnulll 2.M=Smulll Mulham Mob-Maahummm. Lengy, LoLange
Table 11: Statistical comparison of preference bias across image reward models relative to a CLIP baseline. For each metric, report the mean value under CLIP and under CLIP augmented with given reward model (HPSv2.0, HPSv2.1, or PickScore), along with the difference A MCLIP+RM MCLIP. Statistical significance is assessed using paired tests across the same set of verbs, with p-valucs indicating the reliability of observed differences and Cohen's 's d quantifying standardized effect size. Significance levels are denoted as '''p= < 0.001, "*p < 0.01, and "p- < 0.05. Effect sizes are categorized as Small (ld) < 0.2), (0.2 |d| < 0.5), Medium-Lare (0.5 < [d] < 0.8), and Large (ld) 2 0.8). PBSg measures ethnicity-aware gender bias, RDS, captures deviation from uniform ethnic representation, and SDI reflects overall cthnic diversity (higher is more balanced). Missing entries (-) indicate cases where metrics are undefined due to zero observable instances for the corresponding subgroup.
Statistical Analysis of Reward Models. We further conduct a paired statistical analysis to quantify how different image reward models deviate from the CLIPP baseline, with results reported in Table [1 For each
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metric, we compute the difference A Preward Mowward-UCLPP and assess statistical reliability using p-values, alongside Cohen's s d to measure practical effect size. Overall, the three reward models exhibit markedly different. bias profiles relative to CLIP. CLIP + CLIP+ shows the most dramatic and systematic shifts: 14/16 metrics differ significantly from CLIP (p < 0.05), with consistently large effect sizes across ethnicity- aware gender bias metrics. In particular, the averaged PBSg increases sharply (A = +0.676, p < 0.001, d= 1.59), and all ethnicity-ppecific PBSg scores exhibit large, statistically robust changes, indicating a strong amplification of gender preference patterns encoded by HPSv2.0. In contrast, CLIP + CLIP+HPSV2. yields fewer significant deviations (10/16 metrics), with substantially smaller magnitudes; notably, the averaged PBSg change is not statistically significant (A = -00026, p 0.6208, d = 0.08), suggesting that HPSv2.1 induces more moderate and heterogeneous bias shifts. CLIP + CLIP+PickScore produces the weakest overall departure from CLIP, with only 7/16 metrics reaching significance and no significant change in averaged PBSg (A = -0.043, p= = 0.4117, d = 0.13), although several ethnicity-speciffc RDS, metrics still show medium-to-large effects. Across all three reward models, changes in SDI are consistently small and statistiallly insignificant, indicating that reward modeling primarily redistributes bias across demographic groups rather than altering overall ethnic diversity. Taken together, these results demonstrate that reward model choice plays decisive role in shaping downstream bias characteristics, with HPSv2.0 exerting the strongest and most systematic inffuence, while HPSv2.1 and PickScore induce comparatively weaker and more selective bias shifts.
(a) CLIP (gray) vs. HPSv2.0 (teal) (b) CLIP vs. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 19: Ethnicity-aware aware gender bias for the White subgroup across 42 verbs. We compare the baseline im- age reward model CLIP with its preference-alineed variants (HIPSv2.0, HPSv2.1, PickScore). Values are shown relative to zero-centered neutral zone; regions outside this zone indicate systematic gender bias, with positive values corresponding to man-preference (+) and negalice values corresponding to woeann-refeeence (-).
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(a) CLIP (gray) vs. HPSv2.0 (teal) (b) CLIP vs. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 20: Euhnicity-aware gender bias for the Black subgroup across 42 verbs. We compare the baseline image reward model CLIP with its preference-aligned variants (HPSv2.0, HPSv2.1, PickScore). Values are shown relative to zero-centered neutral zone; regions outside this zone indicate systematic gender bias, with posilive values corresponding man nan-preference (+) and negative values corresponding to woman- prefen-eeeeeecce (-).
(a) CLIP (gray) vs. HPSv2.0 (leal) (b) CLIP vs. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 21: Sthnicity-awarr gender bias for the Latino subgroup across 42 verbs. We compare the baseline im- age reward model CLIP with its preference-aligned variants (HPSv2.0, HPSv2.1, PickScore). Values are shown relative zero-centered neutral zone; regions outside this zone indicate systematic gender bias, with positive values corresponding man-preference (+) and negative values corresponding to woman- prefeeecee (-).
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44
80
(a) CLIP (gray) vs. HPSv2.0 (teal) (b) CLIP vs. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 22: Ethnicity-aware gender bias for the East Asian subgroup across 42 verbs. We compare the baseline image reward model CLIP with its preference-aligned variants (HPPSv..0, HPSv2.1, PickScore). Values are shown relative to zero-centered neutral zone; regions outside this zone indicate systematic gender bias, with positive values corresponding to man -an-prefeeence (+) and negative values corresponding to woman-preference (-).
(a) CLIP (gray) HPSv2.0 (teal) (b) CLIP vs. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 23: Ethnicity-aware gender bias for the Southcast Asian subgroup across 42 verbs. We compare the baseline image reward model CLIP with its preference-alignel variants (HPSv2.0, HPSv2.1, PickScore). Values are shown relative to zero-centered neutral zone; regions outside this zone indicate systematic gender bias, with positive values corresponding to man-preference (+) and negative values corresponding to woman-preference (-).
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(a) CLIP (gray) vs. HPSv2.0 (teal) (b) CLIP vS. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 24: Ethnicity-aware gender bias for the Indian subgroup across 42 verbs. We compare the baseline im- age reward model CLIP with its preference-aligned variants (HPSv2.0, HPSv2.1, PickScore). Values are shown relative to a vero-centered neutral zone; regions outside this zone indicate systematic gender bias, with posilive values corresponding to man prefereeee (+) and negative values corresponding to woman woman-preferenee preference (-).
(a) CLIP (gray) vs. HPSv2.0 (teal)
(b) CLIP vs. HPSv2.1 (yellow)
(c) CLIP vs. PickScore (orange)
Figure 25: Ethnicity-awarr gender bias for the Middle Eastern subgroup across 42 verbs. We compare the bascline image reward model CLIP with its preference-aligned variants (HPSv2.0, HPSv2.1, PickScore). Values shown relative zero-centered neutral zone; regions oulside this zone indicate systematic gender bias, with positive values corresponding to man-preference (+) and negative values corrosponding to woman-preference )
0.6
Ethnicity
east asion
inciian
0.4
southeast asian
white
bake bie call clean
cook coogg
by
dfink drive
indrcise
breed Maaee
mille
stard
plare
crewtht (uac perer Meriin widd
Figure 26: Ethnicity representation bias CLIP across 42 verbs.
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0.6
Edhniity
tack
east sian
m middle castem
southeast astan
white
80
like Cal
dleen cima cook
cougr
city
arrok
afr
fatiing Ree
jump
knewt
stop dooe
alepp
amita
sland
stratch
mever
Figure 27: Ethnicity representation bias in HPSv2.0 across 42 verbs.
Edhnicity
Sandarcied
black
asian
Indian
0.4
latino
southeast asian
02
white
bate
bile
Call
clean
clim
cook
dripe drive
eat
acercise faning
Jump Kick
Krast
paint
pice
priot
shep
stove
Figure 28: Ethnicity representation bias in HPSv2. across 42 verbs.
1.0
0.8
Fihnirity
Black
latino
middle eastemm
southeast
60
bike
tike
dlem
cimb
cook
Gough
arink
drive end
seerche futting
fat
Jurpp
Kirk
Ireest
tmugh
Daint
plck
ran
stop ahout
alade sleap amite
stand
dew
seretch
sudy
mreep
Figure 29: Ethnicity representation bias in PickScore across 42 verbs.
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D Social Biases in Preference Alignment
Building on our analysis of gender and ethnicity biases in image reward models, we examine how preference aligmment tuning affects bias in video generation. We fine-tune a Video Consistency Model distilled from VideoCrafter=V2 (VCM-VC2) (Li et al., 2024) using three image-text reward models, HPSv2.0, HPSv2.1, and PickScore, and compare social bias distributions before and after tuning to assess how each reward model shapes identity representation. Following the T2V-Tubbo-V V1 training protocol (Li ct al. 2024), we incorporate reward feedback into the Latent Consistency Distillation process (T.uo al. 2023 by using single step video generation. During student model distillation from pretrained teacher text to video model, we directly optimize the decoded video frames to maximize reward scores from the image-text alignment models, guiding each frame toward representations more aligned with human preferences.
We evaluate aligned video diffusion models using our bias framework ($4). Table S reports two metrics: PBSG for gender imbalance across ethnic groups, and RDS, and SDI for ethnicity representation disparity and overall output diversity.
Ethnicity-Aware Gender Bias. We evaluate gender portrayals under the ethnicity+person condition using the previously defined PBSg metric. A positive PBSg score indicates a lendency to depict men more frequently, while a negative score suggests a preference for women. The base model, VCM-VC2, demonstrates a strong man bias across all ethnicities, which becomes more pronounced with aligmment using HPSv2.0. In contrast, aligument with HPSv2.1 and PickScore significantly reduces PBSg, indicating a shift toward more balanced or woman-preeerres outputs. This change reflects the underlying woman bias present in the HPSv2.1 and PickScore reward models, which steer the model away from the man-dominant bias of the base FidurFiggures 30 to 37 presents the PBSg scores across 42 verbs for cach ethnicity group.
Ethnicity Bias. Under the ethnicity-only condition, we analyze models' representation balance using the previously defined RDS, and SD1 metrics. Positive values indicate overrepresentation, and negative values indicate underrepresentaatinn Overall demographic balance is measured using SDI, where higher values reflect more equitable representation. The base model, VCM-VC2, strongly favors White individuals (RDS = 0.6405), while Black, East Asian, and Middle Eastern groups are underrepresented. Alignment with HPSv2.1 reduces some disparities by improving balance for White and Black groups, but significantly decreases Latino representation (RDS- -0.4352) and lowers SDI, indicating reduced diversity. In contrast, PickScore achieves the highest SD1 and produces more balanced representation across most ethnic groups, resulting in the most demographically equitable outputs. Figure 38 shows the ethnicity bias across 42 verbs.
Statistical Analysis of Aligned Video Models. We analyze the impact of alignment tuning on VCM-VC2 by comparing the base model against versions aligned with different reward models (HPSv2.0, HPSv2.1, and PickScore), with paired statistical results reported in Table 12 For each metric, we compute A Maligned - Pbaas and evaluate statistical reliability using p-values alongside Cohen's 's d to quantily effect. magnitude. Overall, alignment tuning induces large and highly systematic shifts in ethnicity-aware gender bias, with both the direction and magnitude of change strongly dependent on the reward model. Aligument with HPSv2.0 results in consistent increase in male bias, with 8/11 PBSg metrics showing statistically significant changes; in particular, the averaged PBSg increases by +0.1088 (p- = 0.0003, d = 0.61), with medium-to-large effect sizes observed across most ethnic groups. In contrast, aligmment with HPSv2.1 produces pronounced reduction in gender bias, with 11/12 metrics reaching statistical significance and uniformly large effect sizes; the averaged PBSg decreases sharply by -0.577 (p < 0.001, d = 1.72), and all ethnicity-speeific PBSg scores cxhibit large, highly significant declines. Similarly, PickScore- based alignment substantially reduces gender bias, with 8/14 metrics significant and a large decrease in averaged PBSg (A = -0.432, < 0.001, d = 1.39), though the magnitude of reduction is more heterogeneous across ethnic groups. Changes in ethnic representation (RDS.) further reveal reward-specific redistribution effects most notably, large reductions in White overrepresentation and Middle Eastern deviation under HPSv2.0 and 1PSv2.1-whit overall diversity as measured by SDI remains statistically unchanged across all alignment. variants. Taken together, these results demonstrate that aligument tuning is a powerful intervention that can either amplify or substantially mitigate gender bias in video generation models, with most effects achieving strong statistical significance and large practical impact, underscoring the central role of reward model choice in shaping downstream social bias outcomes.
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Metzic
VCM-VC2
HPSv2.0
HPSv2.1
pival
Sig.
p-v
Slg.
p-val
Sig.
Eddnieily -Awer Gender Bius
Proportion
Score for
(PDSo)
Average
0.83
0.812
+0.100
0.000
-0.61
0.227
-0.527
0.000
1.72
0:371
-0.332
0.000
1.39
White
0.792
0.967
10.174
0,000
-0.67
XLL
Yeshne
0.132
-0.000
0.000
1.61
Yoohee
0.341
-0.450
0,000
1.36
Yestne
Dlock
0.776
0.821
+9.146
0,100
-0.67
M-L
Yog!na
0,2288
-0.538
01000
1.50
Yosh
0.330
-0.480
0,000
1,25
Yes
Iodian
0,863
0.800
+0.057
0.251
-0.17
No
0.145
-0.718
0.000
2.64
Yiatre
0.250
-0.615
0,000
1.48
Yist
Fost Axlan
0.712
0.850
+0.159
0.010
-0.92
S-M
Yee"
0.157
-0.55
0.000
1.56
Yieh
0.115
-0.567
0.030
1.34
You
Yea
Yietre
You
4..19
-0,22
Yogh
0,850
0.967
+0.038
0.000
Yistte
0.374
-0,455
0.000
You
-0.150
0,000
Fihasertt Blar: Regrosntttion Drolstoo Scom for Elbeirity (RDS.)
Black
-0.188
-0.10:
-0.002
0.650
0.07
No
Fost Aslan
-0.158
-0.118
-0.000
0.150
0.22
S-M
No
Latino
-0.155
-0031
-0.136
0.000
5,04
1
Yost
-0L188
10.037
0.183
-0.21
S.3
No
Mildle Enstarn
-0.17
-0:307
-0.290
0.000
1.26
Yia
-0,264
-0.148
0.000
1.0
1.
Yieite
-3.110
+0.007
0.740
-0.05
No
White
Yis
0.505
Yiarre
Edhadnill
Simponn's Divunilyy (991)
00022
00,00
No
-0.003
Siee SwSnelt S Saaallll Madion, M. MrC. farge, Cazagge
Table 12: Statistical impact of alignment tuning on bias metrics in the VCM-VC2 video generation model. We compare the base VC2 model against versions aligned with different reward models (HPSv2.0, HPSv2.1, and PickScore). reporting mean values, alignment-induccd differences A = Paligned Mbase: paired p-values, and Cohen's d effect sizes. Statistical significance is assessed using paired tests across the same set of verbs, with p-values indicating the reliability of observed differences and Cohen's 's d quantifying standardized effect size. Significance levels are denoted as **xp pp < 0.001, ""p < 0.01, and "p < 0.05. Effect sizes are categorized as Small (ld) < 0.2), Small-Medium (0.2 S [d] 0.5), Medium-Large (0.5 S |d] < 0.8), and Large (ld) 2 0.8). PBSg measures ethnicity-aware gender bias, RDS, captures deviation from uniform ethnic representation, and SDI reflects overall ethnic diversity (higher is more balancod). Missing entries (-) indicate casos where metrics are undefined due to zero observable instances for the corresponding subgroup.
HPSxd.1 Paal-Ttennd
PoScore hoa-Thuned
Reattl (965-
Nerinl (96
ride
we
ride
welk
shoue
cry
shoutt
skote
dean
dea
work
shop
work
20p
jump
pray
jamp
bake
Gretch
babe
50
bike
bike
Co
4.43 E.y
0rink
drink
0fve
w.0h
wach
point
paint
slck
study
PIcK
stud)
xtile
smil
stand
stare
pitch
state
pitch
Chl
Stercie
knoel
laugh
cook:
exercise
kneat bugh
(a) VCM-VC2 (gray) vs. C (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+ PickScore (orange)
Figure 30: Ethnieity-aware gender bias for the White subgroup across 42 verbs. We compare the unaligned baseline video diffusion model VCM-VC2 with its preference-alignee variants trained using HPSv2. and PickScore rewards. Scores are plotted relative to zero-centered neutral zone; values outside this region indicate systematic gender bias, with positive values denoting man-preference (+) and negative values denoting woman- preferecce (-).
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bacdine RodiTovreed
booeliee
Putiiniee
(182w2)
Haute (720md
now wash
row mash
stane
paint
stare
paint
study
oat
study
clmb
rough
clibb
cough
clean
pray
clean
pmay
step
kick
read
shop
re3d
Shop
3,00
thro
throw
bake
work
2,02
call
cel
COOK
27OUT
Cook
chout
drnn
drekk
In
picch
sich
46
skate
bugh
state
lough
Ammlle
knww
stille
theel
Bic drive
hit
bike
orive
hit
(a) VCM-VC2 (gray) vs. VCM-VC2+ (yellow) (b) VCM-VC2 (gray) VCM-VC2+ (orange)
Figure 31: Ethnicity-aware gender bias for the Black subgroup across 42 verbs. We compare the unaligned bascline video diffusion model VCM-VC2 with its preference-aligncc variants trained using HPSv2. and PickScore rewards. Scores are plotted relative to a zero-centered neutral zone; values oulside this region indicate systematic gender bias, with positive values denoting man- man-rreferenee (+) and negative values denoting woman-preference
Mexial (1e5-1)
Narro 0955-51
Firow ride
Shrow
ride
pray
pray
stt
plck
pick
bibe
babe
bike
bakc
Clean
shop
clean
shop
drink
sleep
drink
kkpp
SWOOD
1dck
SWOSD
Log 25
Kick
6,22
<lmb
clibb
03R
20
COt
shout
S00FC 64
read
pitch
laugh
smile
smile
study
drive
stady
drive
(a) VCM-VC2 (gray) vs. VCM-VC2+HPSvV7 (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+ PickScore (orange)
Figure Ethnicity-aware gender bias for the East Asian subgroup across 42 verbs. We compare the unaligned baseline video diffusion model VCM-VC2 with its preference-aligned variants trained using HPSv2.1 and PickScore rewards. Scores plotted relative to zero-centered neutral zone; values outside this region indicate systematic gender bias, with positive values denoting man-preference (+) and negative values denoting woman-preference (-).
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Sendite
beseinn
(RSea Frak-Tunrd (755m))
paint
ride
Rrk
jump
paint
ride
jump
stare
desn
stare
ciean
bike
bake
eat
shop
eat
stop
pick
pray
pray
kneel
50
knedl
Ofive
25
stand
rratch
sand
swep
wwep
thro
study
call
cell
<lmb
cook
1Un
cook
cough
FOW
cough
T0W
Lough
#t
bugh
in
(a) VCM-VC2 (gray) vs. VCM-VC2+HPSV22 (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+ (orange)
Figure 33: Echnicity-aware gender bias for the Southeast Asian subgroup across 42 verbs. We compare the unaligned baseline video diffusion model VCM-VC2 with its preference-alignel variants trained using 1PSv2.1 and PickScore rewards. Scores are plotted relative to a zero-centered neutral zone; values outside this region indicate systematic gender bias, with positive values denoting man-preference (+) and negative values denoting woman-preference (-).
Bosiin
drink Nompp clibh
seep
stare
store
eoencie
pray
exar lae
play
paint
baik
paint
bake
road
chile
622
smile
0.50
dffic
4.25
walk
retch
wilk
mush
bibe
brb
50
2000
cook
coul
mth
ht
bick
ridw
kick
laugh
pitch
bugh
pitch
(a) VCM-VC2 (gray) vs. VCM-VC2+ (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+PPccSSoom (orange)
Figure 34: Ethnicity-aware gender bias for the Indian subgroup across 42 verbs. We compare the unaligned bascline video diffusion model VCM-VC2 with its preference-aligned variants trained using HPSv2. and PickScore rewards. Scores are plotted relative to zero-centered neutral zone; values oulside this region indicate systematic gender bias, with positive values denoting man-preference (+) and negative values denoting woeeen-reeeeence (-).
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Boredie
p45d poriiared hestra (95--4
WiScree hett huttiiiiiess
climb
call stand shop
dlibb
call stand
shop
red
onnnk
exerdse
bake
exerdos
bake
paint
pray
paint
pray
PeX
pick
ride
slepp
11cc
sleep
state
LEt
kick
smil
1mm
1mm
10000
throw
1000
Wash
mach
MODK
shout
Work
2hout
cook
10M
cook
rOW
cough
rish
leugh
fist
tnugh
(a) VCM-VC2 (gray) vs. VCM-VC2+HPSV2.1 (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+ (orange)
Figure 35: Ethnicity-aware gender bias for the Latino subgroup across 42 verbs. We compare the unaligned baseline video diffusion model VCM-VC2 with its preference- variants trained using HPSv2.1 and PickScore rewards. Scores are plotted relative to zero-centered neutral zone; values outside this region indicate systematic gender bias, with posilive values denoting man-preference (+) and negalive values denoting woman preferenee (-).
Nadtral (FS5-1)
Reatra (P25-2)
skate jumparciie
SR
piate
red
read
pitch
pich
bike
stand
p(2y)
stand
BRy
0.75
study
sough
stddy
50ugh
UNEO
drive
throw
Cill
shout
Call
shout
drink
ridc
dfncc
rid0
pick
fth
plcx
kmowl Iaugh
creel leugh
(a) VCM-VC2 (gray) vs. VCM-VC2-HPSS2. (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+PPckSSomm (orange)
Figure 36: Ethnicity-aware gender bias for the Middle Eastern subgroup across 42 verbs. We compare the unaligned baseline video diffusion model VCM-VC2 with its preference-aligned variants trained using HPSv2.1 and PickScore rewards. Scores are plotted relative to zero-centered neutral zone; values outside this region indicate systematic gender bias, with positive values denoting man- nan-preference (+) and negative values denoting woman-preference (-).
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Borelie
Boociee
sough
read
jump
PickSough read
jump clean
stane
clear
Mare
stand
paint
stand
paint
622
eat
CPy
pra
pray
0.75
shop
19A
shop
tatch
Wash
retch
WORK
15
W00K
ough
orink
15hh
dinne
blach
exerdee
ptch
dimb
stdde
sbote
bike
smile
smile
(a) VCM-VC2 (gray) vs. VCM-VC2+HPSV2.1 (yellow) (b) VCM-VC2 (gray) vs. VCM-VC2+ PickScore (orange)
Figure 37: Euhnicity-aware gender bias (averaged) for all subgroups across 42 verbs. We compare the unaligned baseline video diffusion model VCM-VC2 with its preference-aligneee variants trained using HPSv2.1 and PickScore rewards. Scores plotted relative to zero-centered neutral zone; values outside this region indicate systematic gender bias, with posilive values denoting man-preference (+) and negative values denoting woman-preference (-).
Baseline
1.0
Nurcmaage
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Nrrcanaage
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HPSv2.1 Past-Trained
cast asia
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middle castem
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Nicertage
Dake
bie
Cal/
cremn
cimo
cook
drink
Gaint
pick
pray
rotl
refe
shop
alare
walk
most
Figure 38: Ethnicity bias distribution across models. Shown the unaligned baseline video diffusion model VCM-VC2 and its preferenco-aligned variants trained with HPSv2.0, HPSv2.1, and PickScore reward signals.
43

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Published in Transactions on Machine Learning Research (02/2026)
E
Controllable Preference Modeling for Video Diffusion Models
Building prior findings, we observe that reward models trained imbalanced image preference datascts inherit and amplify social biases. These biases are then reflected in video dilf'usion models fine-tuned with such reward signals, often leading to unbalanced outputs. In this section, we explore whether manipulating the distribution of social attributes in image datasets allows for controllable bias in reward models, enabling video models to produce more equitable outputs (Sheng et al. 2020
E.1 Image Reward Dataset Construction
Building on the generated images from 5.1 we construct two case-specific reward datasets: one with a man-preferred bias and the other with a woman- prreeerre bias. The man-preferred dataset is designed to steer both the reward model and the downstream diffusion model toward favoring man representations. Conversely, the womann-peefered dataset encourages shift toward woman representations. Notably, when applied to a base video diffusion model that exhibits man-preference bias, the woman-preferred dataset can serve effective counterbalance, enabling the training of models with more equitable gender representation.
More specifically, we construct preference pairs using images from the Gender+Ethnicity dataset by selecting two images that depict the same action and belong to the same ethnicity group, one featuring a man and the other a woman (for example, images M-1 and W-1 in Figure 5). These image pairs are used to train reward models with prompts of the form: "A/An [ethnicity] person is [action]-ing -ing [context] " For the man-preference dataset, we assign a reward score of to the image with a man character and 0 to the image with woman character. In contrast, f or the woman- poman--reference dataset, image with a man character and to the image with a woman character. This process results in 2.94 million preference pairs in each dataset, calculated as 42 verbs multiplied by seven ethnicity groups, with 100 male and 100 female images per group. To improve the reprosentation of no-face content, we additionally incorporate 537,660 face-free image pairs from HPDv2, which enhances balance in our proposed reward datasets.
assign reward score of 0 to the
E.2 Image Reward Model Development & Alignment Tuning
Leveraging the man-preferred and preanepre image datasets introduced in 77.1 we fine-tune two reward models on top of a pre-trained CLIP vision encoder: the Man-Preferred Reward Model (RMM) and the Woman-Preferred Reward Model (RMw). Each model is trained to reflect gender-specific preferences based on its respective dataset. As shown in Table 7 RMM consistently assigns greater PBSg scores across all demographic groups, indicating a strong alignment with man-preferred representations. In contrast, RMM exhibits an opposite trend, systematically favoring woman-preferred content. The clear divergence between these models highlights the effectiveness reward tuning in capturing and reinforcing gendered preferences.
Building on our earlier reward model training, we applied RMM and RMw to guide aligument tuning of base video diffusion model using the same preference-driven training strategy. These reward signals enabled the generation of two distinct variants: one aligned with man-preferred content and the other with woman-preferred content. As shown in Table g alignment with RMM led to consistently greater PBSg scores across all demographic groups, reinforcing man-preference bias. Conversely, alignment with RMw resulted in substantially smaller scores, indicating a strong shift toward woman- preffeecee bias. These results confirm that our controllable preference modcling approach can effectively modulate gender bias in vidco generation, offering a flexible mechanism to either amplify or reduce specific social tendencies in model outputs. Figures 39 to 43 presents the PBSg scores across 42 verbs for each ethnicity group.
E.3
Verbs Correlation Analysis
We analyze the changes in the reward model preference for 42 events and the bias of the vidco generation model before and after post-training, using the training results from 77 In Figure 44 the horizontal axis represents the reward model preference (PBSg), and the vertical axis represents the change in the video generation model's bias before and after post-training (A PBSc). In Figure 45 and Figure 46 the horizontal axis represents the event, and the vertical axis represents the change in the video generation model's bias before and after post-training (A PBSa) divided by the reward model preference (PBSc). This ratio indicates
44

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Published in Transactions on Machine Learning Research (02/2026)
FT
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1.14
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(a) We compare the baseline image reward model CLIP (b) We compare the baseline image reward model CLIP (gray) with its man-preferred, preference-aligned vari- (gray) with its woman-preferred, preference-aligeed vari- ants (RMM, teal).
ants (RMw, teal).
Figure 39: Averaged ethnicity-aware gender bias across all subgroups and 42 verbs. Scores are plotted relative to a zero-centered neutral zone; values outside this region indicate systematic gender bias, with positive values denoting man-preference (+) and negative values denoting woman-preference (-).
the sensitivity of particular event to the bias during post-training. We have arranged the events in the figure from left to right in ascending order of the vertical axis values; events further to the right are more sensitive.
F Reward Model Training and Inference Details
For both the training and inference of the reward model, largely follow the settings outlined in (Wu et al. 2023). We also utilize the HPSv2 codebase available at https://githut: httt::///gttubbb..m//xx0/0/////2 commtgxx002/HHSVZ for these processes. For training, we employ batch size of 16 and the AdamW optimizer. The man caau---eferred and woman-preferred datasets that we construct are adapted to the data loading format specified codebase /https://github http::////gituub.oooottot/ wtt.coo///gaaa Co0n/txxx00//HPP222
the HPSv2 Ultimately, we train the reward models for man man- mran-rrfferred and woman-preferred data for epochs (equivalent to 23000 steps), with no data repetition within each step. The model training is initialized from CLIP checkpoint. For inference, we use the CLIP score as the inference score for the reward models.
G Video Model Training and Inference Details
For post-training during alignment tuning, we used the T2V-TubboVV codebase (Li et al. 2024). available at https://github. com/ Ji4chenLi/t2v model is jointly trained with both the reward model loss and the diffusion loss over 200 steps, using data sampled from the Web Vid-10M dataset. For inference, we also utilize the same T2V-Turbo-V V1 codebase. Each inference setting is run 10 times with different random seed to ensure consistency and robustness of the results.
A reward model loss scale of 1 applied. The video
45

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Published in Transactions on Machine Learning Research (02/2026)
Facelin
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(a) Ethnicity-aware gender bias (averaged) of man- (b) Ethnicity-aware gender bias (White) of man crefprrferred preferred (orange) woman-prefers coman-preferred (teal) post-trained (orange) and woman- preeerrre (teal) post-trained video video generation model by reward model RM and RMw. generation model by reward model RM and RMw.
Figure 40: Ethnicity-aware gender bias of man-preferred and woman-preeerred post-trained video generation model by reward model RMM and RMw.
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(a) Ethnicity-aware gender bias (Black) of man-preferred (orange) and woman-preferred (teal) post-trained video preferred (orange) and woman- woman-preferred (teal) post-trained generation model by reward model RMM and RMw.
(b) Ethnicity-aware gender bias (East Asian) of man-
video generation model by reward model RM and RMw.
Figure 41: Ethnieity-aware gender bias of man-preferred and woman-preferred post-trained video generation model by reward model RMM and RMw.
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Published in Transactions on Machine Learning Research (02/2026)
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(a) Ethnicity- gender bias (SE Asian) of man- preferred (orange) and woman-pree prefe-ereeerred (teal) post-trained (orange) and woman-prefereed (teal) post-trained video video generation model by reward model RM and RMw. generation model by reward model RMar and RMw.
(b) Ethnicity-aware gender bias (Indian) of man- nan-preferred
Figure 42: Ethnicity-aware gender bias of man-preferred and woman-prcferred post-trained video generation model by reward model RMM and RMw.
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(a) Eulmicity-aware gender bias (Latino) of man-preferred (b) Ethnicity-aware gender bias (Mid Eastern) of man- (orange) and womanr-referred (teal) post-trained video preferred (orange) and woman-preferred (teal) post-trained generation model by reward model RM and RMw. video generation model by reward model RM and RMw.
Figure 43: Ethnicity-aware gender bias of man-preferred and woman-preferred post-trained video generation model by reward model RM and RMw.
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Published in Transactions on Machine Learning Research (02/2026)
Man Preferred Post Trained Actiors
Woran- Woran-Preeeree Preferred Post traine Actions
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Figure 44: A PBSg of video generation model before and after alignment tuning by RMM and RMw. Results are broken down into verbs. Figure 45 and Figure 46 are based on this figure.
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Figure 45: Sensitive verbs in man-preferred post-training.
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Published in Transactions on Machine Learning Research (02/2026)
1.2
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Figure 46: Sensitive verbs in womaan-peefereed post-training,
49


Reviews:
[{"review_id": "HvxhyBZDcW", "reviewer": "Reviewer_cVGV", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces VideoBiasEval, an event-centric evaluation framework for auditing gender and ethnicity representation in text-to-video diffusion models, and uses it to trace bias flow from human preference datasets to image reward models to alignment-tuned video models. The authors first define an event prompt space (42 actions x 2 genders x 7 ethnic groups), then use an ensemble of VLMs to extract frame-wise social attributes and propose various metrics ($PBS_G$, $RDS_e$, SDI, TAS) to measure gender/ethnicity bias and temporal stability. Finally, they empirically analyze biases in two human preference datasets, several reward models, and multiple video models before/after alignment, and demonstrate controllable preference modeling (man vs woman preferred reward models) to steer alignment outcomes. Representative tables and figures summarizing these results appear throughout.\n\n**Strengths**\n\n- Clear and well-motivated problem: Studying how alignment tuning propagates and stabilises social bias in video generation is novel and important.\n- Comprehensive experimental scope: Multiple datasets, multiple reward models, several video generators, and a controllable synthetic data experiment.\n- Thoughtful metrics and temporal component (TAS), which demonstrates that alignment can make biased portrayals more temporally stable.\n- Great illustrative figures and clear prompt design.\n\n**Weaknesses**\n## Major\n- **Lack of uncertainty estimates / statistical testing for strong claims:** Most reported numbers are averages without standard deviations, confidence intervals, or hypothesis tests. All of the result Tables (2-6) report point estimates and some result changes, but do not report whether observed changes (sometimes on the order of $10^{-2}$) are statistically significant or could be sampling noise (10 draws per prompt). This makes claims such as “alignment amplifies male bias” based on $PBS_G$ increases of 0.04 or 0.0725 extremely weak (see Table 2 and main text). The authors must report variance measures, preferably with CIs and p-values or bootstrap tests for the key comparisons (dataset to reward to video; aligned vs unaligned). In addition, the authors keep changing their number of significant figures and decimal places across tables and text (e.g. sometimes 4, 2, and even 1 decimal places). There's no clear justification for this, which again puts into question the significance of the results.\n\n- **No conceptual justification for the “ideal” $PBS_G ≈ 0$ claim:** The authors state an “ideal model would achieve $PBS_G ≈ 0$ for all ethnic groups, indicating equitable gender representation independent of ethnicity” (Sec. 3.3). This assumes that (a) gender proportions should be independent of action, ethnicity, and context; and (b) the evaluation set should be uniform. Both are non-trivial normative choices and should be justified or relaxed: True (real-world) proportions may vary by action/ethnicity/context and a single “zero” target may not be appropriate for all tasks. Cite supporting literature or clearly position $PBS_G=0$ as a chosen fairness target (not a factual ground truth).\n\n- **Human evaluation details are too thin:** The human verification is described as “three independent annotators” who reviewed 400 videos (and separately 100 images sampled with 77% agreement). It is unclear who the annotators were (authors, crowd workers, experts?), how they were recruited, what instructions and training they received, and how reliability was computed (was the Cohen/Fleiss Kappa computed on which pools?). The small sample size of potential non-independent annotators also weakens the claim that the used VLM ensemble is a reliable judge. Provide larger-scale human annotation (or at least stratified samples) and a full annotation protocol, or remove claims regarding human verification.\n\n## Minor\n\n- The terminology “Action” is overloaded (it can be confused with an RL agent’s action). Consider renaming to “event action” or “verb” or something else.\n- In Section 5.2, what reward label is given to each dataset sample?", "claims_and_evidence_yn": "No", "claims_explanation": "The experimental framework is promising and broad, but the lack of uncertainty quantification and sparse human evaluation prevents strong conclusions about the significance and generality of the reported effects. See Major weaknesses (1–5) above. For example, reported PBS_G increases of 0.04 (Table 2) and 0.0725 (Table 2) could be within sampling noise given only 10 seeds per prompt and no CIs. Also, the claims about the reward models “inheriting and amplifying” dataset bias are plausible but need statistical backing (Tables 4, 5). Human-VLM correlation claims (Pearson and Kappa scores) are promising but require statistical tests to be convincing.", "audience_yn": "Yes", "audience_explanation": "The topic of bias propagation through alignment in generative video models (and temporal stability of bias) is timely and important for ML fairness, generative models, and alignment communities. The framework, metrics, and analysis pipeline may be useful to researchers and practitioners.", "requested_changes": "Please address my weaknesses above. Mainly: \n\n- Add standard deviations/CIs and statistical tests for all key tables/figures. \n- Clarify and standardise numerical precision.\n- Expand and document human evaluation: annotator source, protocol, sample size, agreement details. Or remove these claims.\n- Justify or reframe the assumption that $PBS_G≈0$ is the ideal target.", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "SLaDplSjTj", "reviewer": "Reviewer_AK8h", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This study provides extensive analyses of biases in (1) aligned and unaligned video diffusion models, (2) reward datasets and models with which these diffusion models are aligned/trained, (3) preference alignment by introducing metrics that measure bias in a selected subset of gender and ethnicity. Based on the found results, the paper takes a further step and explores how to control, or mitigate, these biases by training the models with counter-balanced datasets, which indeed results in less biased models.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper clearly motivates their approach early in the introduction, is well-organized and presented clearly. All of the claims made early in the paper are strongly supported by the defined metrics, which feel reasonable for the problem at hand.", "audience_yn": "Yes", "audience_explanation": "I strongly think this paper will draw attention of the broader machine learning community, and in particular people focusing on mitigating bias in large models. The paper not only describes the problem but also attempts to prescribe a solution proposal.", "requested_changes": "Most of my comments are minor recommendations to refine the paper.\n\n- I couldn’t follow the exact execution in Sec. 3.2. Is it the case that we fix [action] and let LLM generate [actor] and [context], and then count [actor]s? Likewise for Table 3.\n- You could consider adding a brief summary of what aligned models correspond to, the general steps done to get the aligned model, etc., since it’s mentioned repeatedly but there is no concrete definition of it.\n- In the caption of Table 2, “we annotate man-preference with (+) and woman-preference with (-),” you rather annotate the shift after the alignment. E.g., it might possibly be misinterpreted at the first lazy glance that entries in the ModelScope row are not biased.\n- Some of the entries are missing in Table 2 (RDS values).\n- Does it make sense to add some easy interpretations of the metrics to the text? E.g., for a 0.5 PBS value, we see three male outputs per female output (3N_female = N_male), to give a sense of the bias. Because it’s not as direct as, say, accuracy, adding this information might improve readability.\n- It would be better if you could annotate the biased directions in figures in the appendices (e.g., the region outside neutral means male bias)—to make them more understandable on their own. You can also annotate the aligned-unaligned information.\n- In Figure 13, the polar axis labels are not perfectly aligned—is it the case that mostly females are rewarded with ‘climb’ action, or rather ‘clean’ action?", "broader_impact_concerns": "The paper does not have a broader impact statement but it might benefit from one. Even though the paper itself does not introduce a new method that might have a broader impact on society, it is analyzing a topic, or rather a problem which is the biases in large generative models, that affects society as a whole. On the other hand, since this is already the main subject of the paper and already discussed in length, I’m not sure if it’s needed.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "DLEUgnPNeS", "reviewer": "Reviewer_2Xc1", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses how alignment tuning with human preference data propagates and amplifies social biases in video diffusion models.\n\nThe authors introduce VideoBiasEval, an evaluation framework combining event-based prompting and vision-language model-based social attribute extraction. They conduct a systematic study tracing bias through human preference datasets. Additionally, they demonstrate that controllable preference modelling can steer models toward more equitable representations.\n\nThe experimental work covers 42 socially-associated actions, 7 ethnic groups, 4 gender categories, and multiple publicly available models. The analysis reveals that biases are inherited and often amplified at each stage of the alignment pipeline, with alignment tuning making biases temporally stable in generated videos.\n\nThe authors introduce the Temporal Attribute Stability (TAS) metric, unique to video generation, showing that higher TAS can correspond to more strongly entrenched biased representations in the models considered.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper's central claims are generally well-supported by experiments and empirical evidence. The main findings—that alignment tuning reshapes social attribute distributions, that biases propagate from preference data through reward models to video outputs, and that controllable preference modeling can mitigate bias—are demonstrated convincingly across multiple models and settings.\n\nThe experimental design is sound, with appropriate controls comparing aligned vs. unaligned models and different prompting conditions. The paper provides comprehensive results with per-action breakdowns in appendices, allowing verification that findings hold across diverse scenarios.", "audience_yn": "Yes", "audience_explanation": "This paper addresses a topic of significant importance to the ML community: understanding and mitigating social biases in generative AI systems. \n\nVideo diffusion models are rapidly advancing with alignment tuning becoming standard practice, and it is important to understand the social implications of these techniques as models approach deployment at scale.\n\nThe framework introduces reproducible evaluation techniques that can be adopted by other researchers working on fairness in video generation. The work bridges machine learning with social science concepts, making it valuable for researchers interested in AI fairness, human-computer interaction, and responsible AI development.", "requested_changes": "While this is a solid paper, there are some discussions (and possible additional experiments) that could strengthen the thesis of the paper even more.\n\n- Is it possible to address the potential consequences of FLUX Benchmark Bias Impact? The paper acknowledges that FLUX-generated benchmark images are only 77% accurate in representing intended social attributes, but there is insufficient discussion of how this contamination affects the reward model preference analysis. Can the authors add analysis of whether misclassification rates differ across demographic groups, and provide sensitivity analysis or discussion of whether main conclusions about reward model preferences would change if analysis were restricted to the validated subset? If complete re-analysis isn't feasible, at minimum add a paragraph in Section 5.2 and/or Limitations explicitly discussing this as a potential confound.\n\n- The authors should expand the VLM evaluator reliability discussion. The 0.73 Pearson correlation for ethnicity classification indicates substantial disagreement (27% unexplained variance) between human and VLM judgments. The authors should improve the discussion on common error modes and when VLMs disagree with humans, including the reasons for these disagreements (perhaps showing example frames where VLMs disagree). Additionally, please discuss whether the 0.73 correlation is sufficient for the conclusions drawn. Is it possible to report results stratified by classification confidence or conduct sensitivity analysis using only high-agreement cases?\n\n- It would be useful for the community to improve the reproducibility of the paper: please either release code and data processing scripts or provide substantially more implementation detail.\n\n- I would have expected a section discussing how the observed biases compare to actual demographic distributions in the training data of the base video models. This context would help interpret whether models are amplifying training data bias or introducing new biases during alignment. Can the authors provide such discussion or experiments?\n\n- For practitioners, I would encourage adding brief guidance on responsible use of the controllable preference modeling approach. This technique should be applied thoughtfully with diverse stakeholder input rather than unilateral technical \"debiasing,\" as different communities may have different perspectives on appropriate representation.", "broader_impact_concerns": "The paper appropriately includes a Limitations section discussing scope constraints and the socially constructed nature of demographic categories. The authors may wish to briefly consider potential misuse scenarios for the controllable bias modeling techniques, though the current discussion is adequate.", "additional_comments": "- Have the authors performed an ablation study on framework components? For example, how do results change with different numbers of sampled frames (currently 16)? What is the contribution of ensembling three VLMs vs. using a single model?\n\n- When is uniform distribution an appropriate fairness baseline and when is it not? Do the authors have insights regarding this? How would different baseline assumptions change the interpretation of the results?", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)