LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Authors: Rashid Mushkani
Shravan Nayak
Hugo Berard
Allison Cohen
Shin Koseki
Hadrien Bertrand
Venue: ICML 2025 poster (2025)
Keywords: Pluralistic Alignment
Text-to-Image Diffusion
Intersectionality
Urban Planning
DPO
Inclusivity
Safety
Accessibility
Primary area: applications->social_sciences
TL;DR: This paper presents LIVS, a dataset of diverse community feedback on public space design, used to fine-tune Stable Diffusion XL. Results show moderate alignment shifts while highlighting persistent subjectivities in AI-generated urban imagery.

Abstract: We introduce the *Local Intersectional Visual Spaces* (LIVS) dataset, a benchmark for multi-criteria alignment, developed through a two-year participatory process with 30 community organizations to support the pluralistic alignment of text-to-image (T2I) models in inclusive urban planning. The dataset encodes 37,710 pairwise comparisons across 13,462 images, structured along six criteria—Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity—derived from 634 community-defined concepts. Using Direct Preference Optimization (DPO), we fine-tune Stable Diffusion XL to reflect multi-criteria spatial preferences and evaluate the LIVS dataset and the fine-tuned model through four case studies: (1) DPO increases alignment with annotated preferences, particularly when annotation volume is high; (2) preference patterns vary across participant identities, underscoring the need for intersectional data; (3) human-authored prompts generate more distinctive visual outputs than LLM-generated ones, influencing annotation decisiveness; and (4) intersectional groups assign systematically different ratings across criteria, revealing the limitations of single-objective alignment. While DPO improves alignment under specific conditions, the prevalence of neutral ratings indicates that community values are heterogeneous and often ambiguous. LIVS provides a benchmark for developing T2I models that incorporate local, stakeholder-driven preferences, offering a foundation for context-aware alignment in spatial design.

LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Rashid Mushkani 12
Shravan Nayak 12 Hugo Berard Allison Cohen? Shin Koseki 12 Hadrien Bertrand?
Abstract
1. Introduction
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, a benchmark for multi- criteria alignment, developed through a two-year participatory process with 30 community organi- zations to support the pluralistic alignment of text- to-image (T2II) models in inclusive urban plan- ning. The dataset encodes 37,710 pairwise com- parisons across 13,462 images, structured along six Aiteria-Accessbilliit Safety, Comfort, Invit- ingness, Inclusivity, and Diversity derense 634 community-defined concepts. Using Direct Preference Optimization (DPO), fine-tune Sta- ble Diffusion XL to reflect multi-criteria spatial preferences and evaluate the LIVS dataset and the fine-tuned model through four case studies: (1) DPO increases alignment with annotated pref- erences, particularly when annotation volume high; (2) preference patterns vary across partic- ipant identities, underscoring the need for inter- sectional data; (3) human-authored prompts gen- erate more distinctive visual outputs than LLM- generated ones, influencing annotation decisive- ness; and (4) intersectional groups assign system- atically different ratings across criteria, reveal- ing the limitations of single-objective alignment. While DPO improves alignment under specific conditions, the prevalence of neutral ratings indi- cates that community values are heterogeneous and often ambiguous. LIVS provides bench- mark for developing T2I models that incorporate local, maakeholder-driverr preferences, offering a foundation for context-aware alignment in spatial design.
Recent advances in text-to-image (T2I) generative modeling have significantly improved image quality and diversity (Bai et al., 2022; Podell et al., 2023; Zhang al., 2024a). These developments can benefit communities by making design processes more wwwsssibbeeeeabling broader engagement among non non-expert stakeholders in architecture, urban plan- ning, and environmental visualization. (Corner, 1999; Pal- lasmaa, 2011; I; Dubey et al., 2024). However, aligning T2I models with the specific needs of local communities remains an open challenge (Qadri al., 2023; Kannen al., 2024; Kirk et al., 2024), particularly when addressing subjective concepts such as inclusivity safety.
derived from
Existing alignment frameworks often rely on large-scale, global data and crowdwork, which may not capture the nu- anced objectives of smaller communities (Dzieza, 2023; Anthropic, 2023; OpenAl et al., 2024; Kirk et al., 2024). Moreover, T21 alignment research has often centered on broad aesthetic content moderation goals (Kirstain et al., 2023; Pressman et al., 2022), while paying limited attention to diverse local criteria in domains where multiple social identities intersect. This gap poses a risk that generative models could systematically exclude or misrepresent histor- ically marginalized groups in depictions of public spaces (Wan al., 2024; Prerak, 2024).
To address these limitations, we propose a pluralistic align- ment approach, wherein alignment explicitly accommodates multiple coexisting norms and values rather than seeking a single universal solution (Sorensen et al., 2024). We intro- duce the Local Intersectional Visual Spaces (LIVS) dataset, which integrates intersectional, community 121-generated images of urban public spaces. Over two-year period, we collaborated with 30 community organi- zations through workshops and interviews, initially collect- ing 634 criteria for inclusive public space design. Through iterative co-creation, these were distilled into six broader categories: Accessibility, Safety, Comfort, Invitingness, In- clusivity, and Diversity.
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'Université de Montréal MMila-Quebec AI In- Rashid Mushkani
stitute. Correspondence to: <rashid.ahmad.nn habama..uuskaaiee...... umontreal.ca>.
We collected 35,510 multi-criteria preference annotations, each covering one to three criteria, fine-tune Stable Diffusion XI. model using Direct Preference Optimization (DPO) (Wallace et al., 2023; Rafailov et al., 2024). We then tested the fine-tuned model with 2,200 additional an-
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
notations, comparing it to the baseline model. In these comparisons, 700 favored the DPO model, 300 favored the baseline, and about 1,100 were neutral, indicating that ex- isting alignment methods are insufficient to capture diverse, subjective preferences and highlighting the need for more nuanced alignment approaches. Our experiments demon- strate that multi-criteria feedback can steer T2I models to- ward locally meaningful outputs, while also showing that no single alignment objective can account for the complexity of community priorities. Additional case studies demonstrate that preference patterns differ across participant identities, prompts result in more visually dis- tinct outputs than those generated by language models, and that intersectional groups provide systematically different ratings across criteria, further illustrating the limitations of single-objective alignment frameworks.
2. Related Work
2.1. Alignment of Generative Models
Multiple datasets have supported alignment efforts in text-to- image (T2I) generation. For instance, Simulacra Aesthetic Captions (Pressman al., 2022) offers 238,000 synthetic im- ages rated for aesthetics, and Pic-a-Pic (Kirstain et al., 2023) comprises over 500,000 preference data points. ImageRe- ward (Xu et al., 2023) extends these efforts by capturing ratings on alignment, fidelity, and harmlessness, while HPS (Wu et al., 2023) and HPS v2 (Wu et al., 2023) propose large-scale binary preference pairs to train reward models reflecting human judgments. Related work in language models has explored moral pecision-making in multilingual contexts (Jin et al., 2024) and contextual preferences across diverse demographics (Kirk et al., 2024), highlighting the importance of subjective, multicultural perspectives in align- ment processes.
that
Contributions:
We introduce the Local Intersectional Visual Spaces (LIVS) dataset, developed through a participatory methodology that captures diverse, community- generated dimensions of inclusive public space design (Berditchevskaia al., 2021; Sloane et al., 2022).
Studies on T2I alignment often focus on aesthetic prefer- ences (Pressman et al., 2022; Kirstain al., 2023; Wu et al., 2023) content policy compliance. However, they typically assume a single, global notion of "goodness" or "suitabil- ity." Pluralistic alignment, in contrast, recognizes that social values are heterogeneous and context-dependent (Turchin, 2019a; Sorensen et al., 2024). Our work extends beyond purely global alignment by collecting specialized, multi- criteria annotations grounded in local, intersectional com- munity knowledge for urban public space design.
We propose a pluralistic alignment framework for text- to-image (T2I) generative models, focusing intersec- tional and locally specific criteria within urban public space contexts. This framework underpins the con- struction of the LIVS dataset, tailored for the urban planning domain.
Fewer efforts target image-based generative models com- pared to alignment in large language models (Bai et al., 2022; Huang et al., 2024b). Prior research has primarily utilized reward modeling and reinforcement learning from human feedback (Stiemnon et al., 2022; Ouyang et al., 2022), with a focus on or factual correctness (Kirk et al., 2024; Jin et al., 2024). In contrast, our approach leverages multi-criteria annotations to inform alignment in T2I outputs within an urban planning context, though these signals are ultimately aggregated into a single binary label for model training via majority voting.
We provide empirical evidence that DPO fine-tuning can modulate image generation according to multi- criteria feedback, yet the prevalence of neutral re- sponses indicates the continued insufficiency of current alignment methods to capture complex intersectional preferences, underscoring the need for approaches ca- pable of accommodating nuanced, diverse user needs. (Fan et al., 2023; Casper et al., 2023; Li et al., 2024; Rafailov et al., 2024).
tasks such as helpfulness
We demonstrate the influence of participant identities on model preferences and compare and Al-generated prompts, highlighting the importance of accommodating local pluralism and human creativ- ity in T2I alignment.
2.2. Intersectionality and Local Knowledge
Intersectionality recognizes that individuals may experience multiple, overlapping forms of marginalization, affecting how they engage with public spaces and technology (Cren- shaw, 1989; Bestanza-Chock. 2020). In generative mod- eling, this perspective is often overlooked, with systems calibrated to an "average" user profile that can obscure the distinct needs of marginalized groups (Benjamin, 2019; Ge- bru et al., 2021; Kirk et al., 2024; Murgia, 2024). For urban planning tasks, ignoring intersectionality risks overlooking critical insights related to accessibility, safety, or cultural expression. Integrating local knowledge adds further granu-
We envision our approach bridging the gap between purely global alignment strategies and the need for more fine- grained methods that incorporate local, intersectional values in real-world applications. In the following sections, we con- textualize related work, detail our methodology, describe our alignment experiments, and discuss broader implications for the field of machine learning.
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
larity, accounting for the historical, spatial, and communal context that global datasets typically lack (Fischer, 2000; Nekoto et al., 2020; Mohamed et al., 2020; D'Ignazio & Klein, 2020). While some studies acknowledge the impor- tance of localized, context-specific input (Aroyo & Welty, 2015; Sloane, 2024; Sieber et al., 2024b), few systemati- cally address intersectionality T21 alignment. By weaving intersectional considerations into local community-driven annotations, our approach endeavors to reflect broader range of perspectives and needs, moving beyond singular, nne-size-fits-all criteria in generative modeling.
human preferences and optimize diffusion models for im- proved alignment with human expectations. Furthermore, Kublmann-Joergenseen et al. (Kuhlmann-Joergense, et al., 2025) emphasized the limitations of simplistic preference annotations, advocating for richer human feedback mecha- nisms to refine T2I model performance and safety.
Building on these developments, we extend multi-criteria preference learning to intersectional urban design goals. We employ the DPO method (Wallace et al., 2023; Rafailov et al., 2024) to fine-tune T2I model using pairwise pref- erence data that account for accessibility, safety, comfort, invitingness, inclusivity, and diversity. By integrating struc- tured human feedback across multiple criteria, we aim to align generative models with complex societal values in urban planning.
2.3. Visual Generative Modeling for Urban Spaces
Urban planning and design have long leveraged visualiza- tions to communicate design objectives and gather feedback from stakeholders (Corner, 1999; Dubey et al., 2024; Guridi et al., 2024). T2I models offer the promise of more rapid prototyping and inclusive deliberation, especially when non- experts can directly prompt a model to generate conceptual designs of plaza, park, or street (Guridi al., 2024; Dubey et al., 2024). Yet. generative models frequently default to learned global priors. potentially reproducing biases or ne- glecting local cultural markers (Hanna et al., 2024; Jin et al., 2024; Kirk et al., 2024; Sorensen et al., 2024). Our LIVS dataset is explicitly curated to capture local, intersectional preferences in a domain where the geometry, aesthetics, and sociocultural elements of a public space are all crucial (Ja- cobs, 1961; Gehl & Svarre, 2013; Mitrašinović & Mchta, 2021; Conitzer et al., 2024; Guridi et al., 2024; Dubey et al., 2024). This approach aids in systematically evaluating how T21 alignment can be guided by multiple, sometimes con- flicting, user-defined criteria.
3. Methodology: Building the LIVS Dataset
We employed a community-based participatory approach to integrate the local perspectives and experiences that are often absent in top-down, universal datasets (Sieber et al., 2024a; Hosking et al., 2024). This methodology positions community members as collaborators, allowing for the iden- tification of context-specific needs and priorities, such as nu- anced understandings of accessibility and safety (Arnstein, 1969; Anttiroiko & de Jong, 2020; Mushkani et al., 2025a), By involving diverse local organizations throughout the pro- cess, we aimed to capture intersectional viewpoints and mit- igate the risk of imposing external definitions of inclusive design (Costanza-Chock, 2020). This approach also fos- ters mutual learning, wherein participants gain insights into Al technologies while researchers obtain domain-specifie knowledge critical for aligning generative models with real- world public space requirements (Engeström, 2014).
2.4. Multi-Criteria Preference Learning
Beyond single-objective alignment, multi-criteria prefer- ence learning integrates multiple attributes into a unified training signal (Liu et al., 2019; Bhatia al., 2021; Fan at.,, 2023; Chakraborty al., 2024). This approach has been explored in text-based RLHF, where models are optimized for multiple constraints, such as helpfulness and safety (Kirk et al., 2024; Jin et al., 2024). Recent advancements extend this paradigm to T2I generation by incorporating diverse human preferences.
3.1. Community Outreach and Engagement
Initial Contacts. We contacted 100 community organiza- tions in a mid-sized city (Montreal, population ~22 million) (Gouvernement du Canada, 2022). These organizations in- cluded neighborhood councils, disability-focused nonprof- its, faith based groups. youth advocacy networks. and other local civic stakeholders. Our objective was obtain de- mographically and experientially diverse set of participants who frequently interact with local public spaces (IRCGM, 2018; Creswell & Creswell, 2022).
Prior work has demonstrated the efficacy of multi- dimensional preference learning in T21. Zhang et al. (Zhang et al., 2024b) introduced the Multi-dimensiona Preference Score (MPS). model trained on over 918,000 human pref- erence choices across more than 607,000 images, capturing multiple evaluation criteria such as aesthetics, semantic alignment, detail quality, and overall assessment. Similarly, Xu et al. (Xu et al., 2023) proposed ImageReward, a reward model trained on 137,000 expert comparisons to encode
Participatory Action Research (PAR). In line with the principles of PAR, our community-bast based approach centers on iterative, collaborative inquiry and reciprocal learning throughout the dataset development process (Israel et al., 1998; Cornish et al., 2023). By involving local organizations as active co-researchers, we ensured that the framing of in- clusion, safety, and other design criteria emerged from lived
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
experiences rather than external prescriptions. This iterative feedback loop aligns with PAR's emphasis collective problem-solving and empowerment, participants guided each stage of data collection and model evaluation while gaining familiarity with T2I technology and its potential applications in urban contexts.
Workshops & Interviews. Over two years, we conducted multiple forms of engagement: eleven workshops, five batches of annotations, and 34 interviews. The process be- gan in 2023 with three nulti--takehooldr workshops aimed at collaboratively defining what "equitable design" means for local public spaces. Participants from diverse back- grounds reviewed images of Montreal's public spaces and discussed attributes they considered most important for in- clusion. Figure shows the distribution of participants self-declared demographics.
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Desnney
Figure I. Distribution of participants' self- demographies. This figure summarizes the demographic profiles (c.g., age, gender, race/ethnicity, and disability) of the individuals who participated in workshops and annotation activities (Mushkani et al., 2025b).
The LIVS dataset was developed to incorporate granu- lar, community-generann
Introductory Sessions (2 workshops, 25-35 partici- pants each): Provided an introduction to Al and T21 technology. Participants also shared open-ended feed- back on the study, AI, and their experiences in local public spaces.
perspectives on accessibil- ity, safety, and inclusivity. By embedding localized knowledge, we aim to reduce the risk of producing uniform design standards that overlook intersectional needs (Low, 2020; Madanipour, 2010; MeAndrews et al., 2023).
Criteria Brainstorming (6 workshops, 28 participants total): We projected 16 images of existing public spaces in pairwise comparisons and asked participants to describe their reactions. This process generated 634 initial concepts related to inclusivity, accessibility, safety, and other aspects of urban design (see Appendix C for additional details).
Tutorial and Feedback (I workshop, 20 participants): Using prompts from the previous workshop, we gen- erated initial images with Stable Diffusion XL (Podell et al., 2023). Four groups, each comprising 3-4 citizens and an urban architect, tested the annotation platform and provided feedback on visual fidelity and repre- sentation before the larger-scale annotation phase (see Appendix C.3, Figure 12 for annotation platform). To optimize data quality and minimize cognitive fatigue, we randomly presented three of the six criteria in each comparison. During pilot trials, participants found evaluating all six criteria difficult, which reduced their sense of progress and visual engagement. The multiple rating elements also constrained image size, making it harder to assess spatial details. Focusing on three criteria enabled more meaningful engagement. Over successive annotation batches, we ensured that all six criteria were robustly evaluated.
Validation (1 workshop, 18 participants + 34 inter- views): The 634 concepts were consolidated into 35 intermediate criteria through merging and se- mantic grouping. Participants then ranked and re- fined these, producing six final Arieeria-Acessiiiiitty Safety, Comfort, Invitingness, Inclusivity, and Diver- sity- on importance and local relevance.
Prompting and Early Feedback.
Prompting (1 workshop, 24 participants): Five groups, each composed of 2-3 citizens, a computer scientist, and an urban architect, collaboratively generated 440 prompts reflecting a variety of public-space scenarios and features. For instance, one group focused on de- signing prompts for pedestrian promenades with green spaces and safe-street initiatives in historical neighbor- hoods (see Appendix E for additional details). While refined prompting techniques can shape generative out- puts, universal keywords alone may fail to capture lo- cal sociocultural and historical contexts (Anttiroiko & de Jong, 2020; Beebeejaun, 2016; Talen, 2012).
Annotations and Evaluation.
Annotations (5 batches, 18 participants): The annota- tion tasks were divided into five batches, each lasting two weeks and containing approximately 750 pairwise comparisons per participant, totaling 42,235 raw com- parisons. In each task, two images were displayed side by side with three randomly selected criteria from the six. Annotators used a slider ranging from -1 (strong preference for the left image) to +1 (strong

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
preference for the right image), with indicating neu- trality. An open-source annotation platform devel- oped to facilitate this process, featuring user-friendly slider interface to accommodate diverse backgrounds (see Appendix D for additional details). A multi-stage data-cleaning process refined the dataset, including re- moval of quality-control annotations and incomplete submissions. The resulting dataset comprises 35,510 high-quality annotations.
phasizing that intersectional dimensions (e.g., disability + race/ethnicity) were woven throughout each category.
3.3. Prompt and Image Preparation
Prompt Collection. Initially, a total of 440 textual prompts describing local public-space scenarios were col- lected from the Prompting workshop. Figure 3 presents a word cloud that illustrates the distribution of keywords within these prompts. To expand the dataset and ensure a suf- ficient variety of prompts, we utilized workshop transcripts and employed a large language model (GPT-40 (OpenAl et al., 2024)) with three distinct prompting strategies (see Appendix C.I for additional details). This process gener- ated approximately 2,910 synthetic prompts, encompassing diverse public-space typologies, amenities, and contexts. We assessed the differences between human-written and LLM --geerrrete prompts using Jensen-Shannol Divergence (Plank & van Noord, 2011), which indicated that all three strategies produced diverse outputs and contributed a comprehensive range of scenarios.
Evaluation (1 workshop): After the annotation phase, we fine-tuned Stable Diffusion XL. using these data. Participants then evaluated the fine-tuned model's out- puts, discussing alignment with local norms and values.
3.2. Criteria Consolidation
The original 634 concepts spanned physical, social, and psy- chological attributes of public spaces (e.g.. lighting, pres- ence of diverse user groups, multilingual signage, seating). Through collaborative merging, collective voting, and it- erative discussion, participants identified six core criteria (Figure 2 illustrates this process):
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Physical and cognitive usability for people of all abilities, including elevators, ramps, tactile indicators, and clear signage.
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Safety: Freedom from crime, hazards, or harassment, often reflected in well-lit areas, clear visibility, active presence, or protective barriers.
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Comfort: Availability of amenities (e.g., seating, shade) and mitigation of environmental conditions (e.g., noise, tem- perature), including cleanliness and spatial shelter.
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Invitingness: Features that encourage people to enter and remain, such as greenery, open layouts, welcoming signage, or visible communal areas.
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Inclusivity: Avoidance of exclusionary design; support for cultural or religious needs; signage in multiple languages. Diversity: Representation of different demographic groups and a range of potential uses.
Figure 3. Word cloud depicting the frequency of various concepts within the 440 collected prompts. The size of cach word reflects its prevalence, highlighting key themes such as public-space typolo- gies, amenities, and contextual use scenarios. This distribution underscores the diversity and contextual comprehensiveness of the prompt dataset.
diverity
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Semantic
Personal Importune reievance
Image Generation. To maximize image diversity and avoid comparisons between images that are too similar, we generated for each prompt up to 20 images using Stable Dif- fusion XL (Podell et al., 2023). We then selected the four most distinct images per prompt using greedy algorithm based on CLIP similarity scores (see Appendix I for addi- tional details). In total, 16,693 images were generated, with subset set aside for quality checks (see Appendix D for additional details), leaving 13,462 images for annotation.
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Figure 2. Distilling the initial concepts into six core criteria. The figure shows how 634 distinct idcas were iteratively merged, dis- cussed, and ranked to arrive final bigh-level categories.
Participants viewed these six criteria as sufficiently com- prehensive to represent inclusive urban design (Anttiroiko & de Jong, 2020; Mitrašinovié & Mehta, 2021), while em-
For each image pair, participants were randomly assigned three of the six criteria and required to annotate at least one criterion. This modification, implemented following
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
feedback from the evaluation workshop, ensured that each criterion had equal opportunity to be evaluated. However, participants reported that certain criteria, such as Inclusiv- ity, were more subjective, leading to fewer distinct prefer- ences and a higher incidence of neutral or equal annotations. Consequently, while each criterion had an equal chance of being selected, some received fewer definitive annotations compared to others. This reflected in Figure 4, where In- clusivity received the fewest annotations, whereas Comfort and Invitingness were annotated more frequently. Overall, the annotation process yielded 37,710 10 image-level compar- isons, amounting to approximately 113,130 criterion-level annotations. Though modest in size by machine learning standards, the dataset prioritizes quality over volume.
ment. Detailed implementation notes (including hyperpa- rameters, prompt generation, and data processing) appear in the Appendix F.
4.1. Case Study I: Does Multi-Criteria DPO Improve Alignment?
Setup. We fine-tuned Stable Diffusiin XL (SDXL; Podell et al. 2023) using Direct Preference Optimization (DPO; Rafailov et al. 2024). Each pairwise comparison in LIVS was treated as a binary "preferred" vs. "not preferred" signal. When annotators provided split ratings across criteria (e.g.. preferring the left image for Safety but the right image for Inclusivity), we collapsed the feedback via majority voting. This procedure trained a single model on all criteria and evaluated its performance on each criterion individually. Model outputs were then generated for a held-out set of prompts and compared against the SDXL baseline.
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Results. Out of 2,200 new comparisons, annotators chose the DPO-aligned model in 700 (32%) instances and the baseline in 300 (14%), marking the remaining 1,100 (50%) as neutral (See Appendix I). Criteria with more training annotations (e.g., Comfort, Invitingness) showed stronger improvements under DPO, whereas Inclusivity and Diversity had higher neutral ratings (Figure 6). Qualitative inspection revealed that DPO produced clearer walkways and seating configurations but did not consistently generate detailed features (e.g., ramps, tactile paving, multilingual signage). Examples of baseline, single-criterion, and multi-criteria fine-tuned outputs are shown in Figure 7. Overall, the results suggest that DPO improves the alignment of T2I outputs with local preferences but continues to exhibit substantial variability across criteria. The high proportion of neutral ratings indicates that DPO alone is insufficient to capture the complexity of community preferences, underscoring the need for more nuanced and adaptive alignment algorithms (see Appendix G for additional details).
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Figure Distribution annotation frequencies for each criterion after data cleaning. Red indicates neutral or equal preferences, while blue represents distinct preferences.
3.4. Ethical Considerations
All activities were approved by a research ethics board and followed a co-production methodology with community organizations (IRCGM, 2018). As part of a knowledge exchange and sepacity--uildiig process, we learned about public space use and perception from participants, while they gained insights into Al and its applications in urban con- texts. Participants were compensated for their involvement and provided written informed consent for data collection. Personally identifiable information was anonymized sure confidentiality (Creswell & Creswell, 2022). During the annotation sessions, some Al Al-generated images exhib- ited biases or inaccuracies (Hosking et al., 2024; Turchin, 2019b), prompting revisions to the data-collection protocol. To promote equitable participation, we developed an ac- cessible web interface that accommodated annotators from diverse backgrounds, including those with disabilities, suring effective evaluation of images.
Additional Observations. Comparing Figure 4 (overall dataset) with Figure (evaluation set) suggests similar distribution of annotations and neutral responses: criteria with more training annotations (e.g.. Comfort, Invitingness) generally exhibit stronger DPO preference in the evaluation. This pattern indicates that additional data may further im- prove multi-criteria alignment through DPO. However, we currently do not leverage neutral labels, and majority vot- ing in multi-criterion comparisons may overlook nuanced disagreements across criteria. Specifically, when annotators provide conflicting preferences across the three assigned criteria, the final label is determined by majority vote-e.g.. two preferences for the left image and one for the right results in a binary label favoring the left. This simplifica- tion disregards the internal variation across criteria and may
4. Experiments with LIVS
We present four case studies examining how multi-criteria feedback from LIVS can guide text-to-image (T2I) align-
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Results. Most participants showed modest preference for DPO, although two late-joining individuals rated the baseline and DPO models similarly (Figure 8). These partic- ipants joined the study after the core workshops and did not participate in earlier collaborative sessions that established the six final criteria. Their equal preference may indicate that less involvement the knowledge-exchand process can lead different or less pronounced alignment percep- LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
obscure which dimensions drive preference. Future work could explore methods for incorporating neutral ratings and resolving multi-label conflicts without collapsing them into binary outcomes. Further analysis of criterion-levee variance provided in Appendix
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Figure 7. Three variants of SDXL outputs generated with the same prompt, seed, and hyperparameters. Left: Baseline SDXL lacks cohesive pathways and shows minimal accessibility features. Mid- dle: An mnclusivityffinttme riers but still has uneven paths. Right: multi-criteria finetuned model shows smoother transitions, clearer walkways, and addi- tional seating.
model adds partial signage and bar-
200
tions. Some annotators who reported mobility challenges were more likely to favor the DPO outputs, suggesting that DPO captured partial accessibility-reltted cues. However, no single demographic factor dominated preferences. This variation underscores the importance collecting intersec- tional data rather than applying universal alignment rule.
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Figure 5. Criteria distribution the evaluation dataset. Neu- tral ratings were more common for Incluiviity and Diversity, indi- cating subtler more subjective distinctions in these categories.
4.3. Case Study III: Does Prompt Composition Affect Rating Consistency?
Setup. We compared images generated from 440 human- authored prompts with four UPT--o-generatee prompt sets. Both baseline SDXL and the DPO-finetuned model were used for each prompt. Annotators then compared image pairs on a random subset of three criteria.
000
Results. Human- prompts (Method 0) led to fewer neutral ratings, suggesting they produced more vi- sually distinct outcomes (Figure 9). In contrast, GPT-4o- generated prompts (Methods 1-4) exhibited higher rates of neutrality, possibly due to reduced contextual specificity, As shown Figure the proportions of neutral annota- tions vary across methods, indicating that prompt design significantly influences annotators' perceptions of alignment differences.
Figure 6. Average DPO preference by criterion. Bars show the tendency toward DPO- oriindd outputs. Performance tends to improve in criteria with higher annotation counts.
4.2. Case Study II: Do Preferences Vary Across Identities?
Setup. We examined whether participant demographies (c.g., disability status, age) influenced choices between DPO-finetuned and baseline outputs. We aggregated each individual's s total count DPO-favoring versus baseline- favoring comparisons across the six LIVS criteria.
4.4. Case Study IV: Do Intersectional Identities Rate SDXL Outputs Differently?
Setup. We explored whether different intersectional iden- tities (e.g., disability status x race/ethnicity) assigned sys- tematically distinct raw scores to images across LIVS cri- teria. Each participant rated images for multiple criteria in separate annotation tasks.
Results. We observed that individuals from various inter- sectional groups (e.g., participants with mobility challenges) typically assigned lower Accessibility or Safety scores. This variation underscores the need for local, intersectional feed-
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
back in T21 alignment for urban planning, single global objective cannot capture such diverse preferences. See Ap- pendix Section H.I, Figures 16 and 17 for further details.
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interpreting neutral responses as alignment failures, we con- sider them indicative of alignment adjustments that were either too subtle or inadequate to capture the complexity of participant feedback. Prompt variations also contributed to this ambiguity, as some prompts yielded less distinct visual differences. Overall, neutral ratings emphasize the impor- tance of richer, context-sensitivy alignment methods that consider both prompt content and multi-criteria data, includ- ing conflicting user needs, and highlight the need for further research (Hosking et al., 2024; Sorensen et al., 2024).
Chtzze's Intersectional Setf-Declared Identity Markers
Figure 8. Boxplot of participant preferences. A score of indicates a tendency towards DPO, while score of indicates tendency towards the base model. Two late-joining participants (asterisks) showed no clear preference, whereas most favored DPO to some extent. One participant with disability indicated smaller margin of preference for DPO, reflecting individual-leww variability.
Methodological Constraints and Alignment Strategy. Because DPO requires binary labels, we collapsed multi- criteria feedback into single preference signals during train- ing. This simplification overlooks intra-sample disagree- ment and limits the model's capacity capture the full complexity of pluralistic preferences. Future alignment strategies should accommodate multi-dimensional annota- tions directly, enabling more nuanced modeling of diverse community feedback.
5. Implications
Our findings inform pluralism and multi-objective machine learning. Implications follow:
Multi-Criteria Overlaps and Conflicts. Some participants valued certain criteria (like Diversity) above others (such as Comfort). Others perceived Safety and Comfort as in- terlinked, finding hard to separate physical hazards from environmental conditions. Future alignment methods might benefit from hierarchical, weighted, context-sensitive, or dynamic objective formulations, capturing partial dependen- cies among criteria (Gabriel & Ghazavi, 2021; Stray, 2020; Li et al., 2024; Sorensen et al., 2024).
Intersectionality and Local Variation. The variations in preference highlight the potential shortcoming of single- objective alignment. Pluralistic alignment attempts unify multiple local perspectives, but fully reconciling them may be infeasible in single model. One potential future di- rection is the development of userrppersonalized alignment layers, where a single base T2I model can adapt to specific subgroups or contexts (Jang et al., 2023; Kirk et al., 2024; Sorensen al., 2024).
Neutral Annotations as Signal. Approximately half of the final evaluations were rated as neutral. While these outcomes may initially suggest lack of meaningful im- provement, they may also reflect genuinely balanced pref- erences or participants' difficulty in discerning visual cues for certain criteria- pacttcculatttrlyyy Inclusivity and Diversity. In several interviews, participants noted that subtle or sym- bolic elements were not always visually salient, Rather than
Comparison with Overton and Distributional Pluralism. Sorensen al. (Sorensen et al., 2024) propose Overton plu- ralism, steerable alignment, and distributional pluralism to address heterogencous human values. Our multi-criteria, participatory framework echoes Overton pluralism by cap- turing a broad range of locally valid design norms, while intersectional feedback supports distributional coverage for
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
varied subgroups. Unlike text-only methods, visuals can convey subtle spatial details (e.g., ramps or diverse crowds) that reduce ambiguity and highlight group-specifie pref- erences. By combining these signals with DPO, we also advance Overton's 's commitment to retaining multiple per- missible viewpoints, although roughly half of our compar- isons remained neutral neutral-reffeecing persistent tensions and underscoring that no single, static alignment fully resolves all local conflicts.
annotations remains modest compared the complexity of T2I generation. Although our results show promising alignment gains, scalability to larger, more diverse regions or to other policy domains is not guaranteed.
Future Work. Future research might explore multi- objective optimization by extending beyond pairwise DPO to address correlated or competing criteria, such as Comfort and Safety, potentially using methods like Pareto optimality or weighted objectives (Chakraborty et al., 2024). Investi- gating neutral annotations could involve developing refined strategies, such as partial reward signals, to interpret neu- tral feedback more effectively instead of discarding it as non- non-informativ.. Additionally, developing tools for policy integration in collaboration with urban planning agencies may facilitate the application of T22 alignment in real-world policy decisions through interactive tools for stakeholders. Enhancing personalization by experimenting with adaptive alignment tailored to specific subgroups or contexts, espe- cially where intersectional dimensions are important, might improve model relevance and inclusivity. Overall, our find- ings suggest that a pluralistic alignment paradigm could be promising for creating locally grounded, inclusive genera- tive Al systems by acknowledging intersectional viewpoints and supporting cuiti-crittrria feedback loops to better serve diverse community needs.
Broader Relevance. While our application domain is urban planning, the core centered, multi-criteria preference data, and DPO-based suming-applics broadly to scenarios where local norms matter (e.g., cultural heritage preservation, healtheare, ed- ucational content creation) (Kirk et al., 2024; Huang et al., 2024a; Hosking et al., 2024; Harland et al., 2024).
6. Conclusion
We presented L/VS, a dataset and methodology for pluralis- tic alignment of T2I models centered on intersectional, local feedback for inclusive public spaces. Our experiments with DPO fine-tuning on Stable Diffusion XL revealed moderate preference gains relative to a base model, especially under criteria such as Invitingness or Accessibility. Nonetheless, roughly half of the annotations were neutral, underscoring the complexity of reconciling diverse criteria and identities in a single generative alignment objective.
Availability. The LIVS dataser-including citizen- provided self-identification markers (with consent) is available for research purposes at mid-space.one. This re- lease aims to establish a benchmark for pluralistic align- ment in text-to-image generation and supports both criterion- specific and user-specific customization. Future work can leverage these granular annotations to develop personal- ized models and adaptive fine-tuning strategies that more effectively address the unique needs of diverse user groups
Contributions. We introduced a multi-year, participatory process that established six locally validated criteria for in- clusive urban design, demonstrated how DPO can incorpo- rate calli--citeeia signals, and provided empirical evidence of partial alignment success. Additionally, we showcased how intersectional feedback can highlight local or demo- graphic nuances that global alignment schemes may over- look. The LIVS dataset and model enable two key use cases: first, supporting empirical research on what constitutes in- clusivity in urban design by providing structured annotations on comfort, accessibility, and other public space attributes; and second, facilitating democratic deliberation in public space renovation projects through visualizations that allow policymakers and communities to explore context-specific interventions. These use cases illustrate how generative models aligned with LIVS can bridge technical capabilities with situated local knowledge, fostering equitable and par- ticipatory urban design. See Appendix B for more details.
Impact Statement
This study adapts T21 alignment to an urban context, aim- ing to better represent diverse demographic perspectives. The dataset and results may facilitate more inclusive public- space design. However, there is a risk of oversimplifying complex social challenges by attempting to visualize in- clusivity and diversity, which are multifaceted. Moreover, potential biases in the annotated data could affect how the model prioritizes certain user groups. We view ap- proach as one step toward more fine-grained, democrati- cally informed generative AI, while recognizing that deeper societal involvement and critical oversight remain essential
Limitations, Our dataset concentrates on one mid-sized, multicultural city, which is well-suited for pluralistic align- ment but restricts the range of local norms represented Other contexts may have profoundly different needs or de- sign principles. Additionally, the total number of partic- ipants was relatively low, and our final test set of 2,200
Acknovledgements
We thank Emmanuel Beaudry- for developing the foundational schema of the Aipithet Platform used during

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
the annotation process. We also acknowledge Toumadher Ammar for her assistance with the workshops, and Jerome Solis for his support with coordination. We are grateful to Sarah Tannir, Leandry Jieutsa, Adèle Kremer, Frédérique Roy, and Roxane Kasprzyk for their contributions.
T., Joseph, N., Kadavath, S., Keriion, J., Conerly, T. El-Showk, S., Elhage, N., Hatfield-Dodds, Z.., Herman- dez, D., Hume, T., Johnston, S., Kravec, S., Lovitt, L., Nanda, N., Olsson, C., Amodei, D.. Brown, T., Clark, J., McCandlish, S., Olah, C., Mann, B., and Kaplan, J. Training a helpful and harmless assistant with rein- forcement learning from human feedback, 2022. URL pttps://azee org/ /2044. 05862.
We appreciate the collaboration our partner organizations and architectural offices, including Enclume, Sid Lee Ar- chitecture, Dark Matter Labs, IVADO, and the Canadian Commission for UNESCO.
Beebeejaun, Y. Gender, urban space, and the right to every- day life. Jourmal of Urban Affairs, 39(3):323 -3444334,44, 2016. doi: 10. 00.108000335216622166 12555526...
This research was funded by the Soutien aux iniliatives avec les collectivités et les entreprises - Collaboration avec les organismes communautaires program of the Université de Montréal, and the Bourse IA ESP. Additional support was provided by Mitacs and the Fonds de recherche du Québec Société et culture (FRQSC) Doctoral Scholarship.
Benjamin, R. Race After Technology: Abolitionist Tools for the New Jim Code. John Wiley & Sons, July 2019. ISBN 978-1-5095-2643-7.
We thank all 30 community organizations in Montreal that supported citizen collaboration. The following were among the most engaged and actively involved: the Congolese Community Center of Montreal, Altergo, La Maisonnée, Cummings Centre, Projet Changement, Women's Center of Plateau Mont-Royal, LGBTQ+ Community Center of Montreal, trrguurrite-Bourgenn Hub, RÉZO, Afrique Féminin, L' L'Agence On est lå!, and the Montreal Women's Groups Table.
Berditchevskaia, A., Peach, K., Gill, I., Whittington, O., Malliaraki, E., and Hussein, N. Collective crisis intelli- gence for frontline humanitarian response, 2021. Techni- cal report.
Bhatia, K., Pananjady. A., Bartlett, P. L., Dragan, A. D., and Wainwright, M. Preference learning along multi- ple criteria: A game-theoretic perspective, 2021. URL https: htt///axxvvv.o//
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This work was enabled in part by computing resources pro- vided by Mila.
Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., Wang, T., Marks, S., Segerie, C.-R., Carroll, M., Peng, A., Christoffersen, P., Damani, M., Slocum, S.. Anwar, U.. Siththaranjan, A., Nadeau, M., Michaud,
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
A. LIVS Dataset Viewer
Select ID
Show Image Pair
2160E3gyfJ177dd4725m
Image
B Image 2
SOMBUT
Label Score
Comfortable
-0.66 (Preference for the image on the left)
Label
Label Score
0.33 (Preference for the image the right)
Label 3
Label Score
Inviting
(Equal preferences)
Prompt
Key transit hub Montreal, adjacent to metro station, vibrant street art, lush greenery, sheltered seating, interactive transit information displays, bike share stations, bustling with commuters and cyclists.
Figure 10. An overview of the dataset, illustrating pairs of images for each prompt alongside corresponding preference scores.
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
B. Use Cases
The multi-criteria alignment approach described in the main paper can adapted for various real-world scenarios in urban planning and beyond. Below, we outline specific applications in which intersectional alignment and rapid text-to-image generation may offer practical benefits.
Community Consultations. Local governments urban planners often conduct participatory design sessions for proposed public spaces. The aligned model can generate visual scenarios that reflect multiple local criteria, such accessibility or safety. Community members can then provide feedback on which visualizations best meet their needs, potentially reducing barriers for stakeholders unfamiliar with technical planning diagrams.
Peer-to-Peer Discussion. Community members can use the model to explore differing priorities in contested designs. By quickly producing multiple variations of a layout, participants can discuss trade-offs (e.g., balancing comfort with affordabilityy without relying on professional mediators.
Rapid Visualization of Ideas. Urban designers and architects can employ the model to iterate on design concepts an early stage, generating a variety sketches that incorporate multi-criteria feedback (e.g., inclusivity or invitingness). This process can reveal overlooked aspects before substantial resources committed.
Teaching and Education. In architecture urban-planninn courses, students interact with the model to see how different prompts and annotation signals affect output images. This hands-on experience can clarify alignment methods and potential biases in generative models.
Amplifying Marginalized Voices. Historically excluded groups (e.g., people with disabilities, marginalized ethnic communities) can utilize the model to communicate spatial requirements, such as multilingual signage or wheelchair accessibility, Visual prototypes allow direct articulation of needs and can lead to more inclusive design outcomes.
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g LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
C. Further Details on Methodology: Building the LIVS Dataset
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Figure Word cloud the collected public-space attributes that were consolidated into six high-level criteria. Larger words appear
more frequently among the original attributes.
C.1. Prompt Augmentation
We collected 440 prompts from annotators, each representing distinct scenarios related to public space typologies, amenities, and ambiances in Montreal. To expand the dataset, we supplemented these with synthetic captions generated by GPT-4o (OpenAI al., 2024). To ensure diversity, we incorporated a wide range of concepts drawn from public space literature, such typologies like parks and wide walkways, amenities such seating areas and streetlights, natural elements like forests and vegetation, locations such suburban Montreal and old industrial ports, people such First Nations and children, architectural elements like duplexes and houses, transportation modes including bike lanes and trams, artistic features such as street murals and art sculptures, different times like winter, nighttime, and Christmas, and animals such as dogs and raccoons. These concepts were used to increase the diversity of the concepts in the generated prompts. We employed three distinet prompting strategies to ensure that the synthetic prompts retained similarity to those generated by humans while covering wide range of topics relevant to public space design. The specific strategies and prompts used for the LLM are outlined below.
Method 1
We randomly sampled 8-16 prompts from the human-generatee set and used them as in co-context examples for the LLM to generate new prompts.
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
System prompt used Method
Your task is to craft detailed and imaginative prompts suitable for diffusion models like Stable Diffusion. These prompts should generate images illustrating the variety Montreal's public spaces, capturing the community's diverse aspirations and values.
Each prompt must be rooted in specific scenario related to Montreal's public spaces. You will be provided with the scenario, keywords, and examples of prompts related to these scenarios. Using this information, your task is to create series of diverse, con textually rich, and relevant prompts following style similar to the ones given as examples. These should aim to generate images showcasing Montreal's 's public spaces from varied perspectives.
We provided the LLM with detailed scenario which was also provided to the annotators during the initial prompt collection phase. Along with this we also provided several keywords related to the public space concepts mentioned earlier. Additionally, we included randomly selected in-context samples relevant to the scenario guiding the model to generate new prompts based on these concepts.
Method 2
System prompt used Method 2
Your task is to craft detailed and imaginative prompts suitable for diffusion models like Stable Diffusion. These prompts should generate images illustrating the variety of Montreal's 's public spaces, capturing the community's ss diverse aspirations and values. To achieve this, you will construct prompts using specific keywords provided for the following categories:
Typology: The type of spaces you want depict
Elements: Distinct elements to include in your scene
Context: The scenarios in which your elements are placed
Style:
The artistic style technique that the image should emulate, defining its visual appearance
Mood: The overall mood or atmosphere of the image
You will also be given a few examples that have been generated using these keywords. Using all this information create complete, coherent prompt similar style to the examples. Aim for creativity and diversity in your prompts, ensuring they cover several aspects of the keywords given. These should aim to generate images showcasing Montreal's public spaces from varied perspectives.
Note:
1. Ensure your prompts integrate some of the provided keywords to encapsulate the community's 'ss desired visions of Montreal's public spaces but ensure that style and length is same as the examples.
Do not mention the style and mood explicitly. Use keywords that bring out these attributes naturally.
The style and the length of the prompts should be similar to the examples given. The prompt should be less than
77 tokens.
This method used template-based approach where specific keywords related to public space concepts in the original prompts were masked. We instructed the LLM to replace these masked keywords with concepts from a wide variety of public space themes. This ensured the prompts closely followed the structure of the human- generated ones while incorporating diverse range of concepts.
Method 3
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
System prompt and incontext sample used Method
Your task is to craft detailed and imaginative prompts suitable for diffusion models like Stable Diffusion. These prompts should generate images illustrating the variety Montreal's public spaces, capturing the community's diverse aspirations and values.
For this task, you will be provided with templated sentence containing several placeholders. Each placeholder repre- sents specific category (e.g., [Typology], [Location], [Activity], [Amenity]). Alongside the templated sentence, you will receive a list of words or phrases corresponding to each category. Your objective is to select the most appropri- ate word or phrase from each list to fill the placeholders, creating a meaningful and grammatically correct sentence.
The structure the templated sentence might require minimal modifications to ensure grammatical correctness and cohesiveness once the placeholders are filled.
Example 1:
Template: a [Typology] for [People] in [Location]
Keywords:
Typology: artistic eco friendly park, pedestrian street, all identities, two-story residential street, park, neighbourhood public space, urban square, wide walkway
People: elderly person, adults, first nations, children, teenagers, adults and elderly people, black and white families, mother and her child, people, various ethnicities
Location: plateau, wellington neighbourhood, old port, montreal 's chinatown, old montreal, Montreal, downtown montreal, mont royal street
Output: neighbourhood public space for children, teenagers, adults and elderly people in Montreal
:more examples;
Divergence (JSD) (Plank van Noord, 2011). Methods and produced slightly higher JSD scores (0.53 and 0.58) than Method 3 (0.40), indicating that all three approaches contributed diverse scenarios, with Method 3 aligning more closely with human style.
To measure deviation from numan-written prompts, we computed the
C.2. Image Generation
We used Stable Diffusion XL generate 20 images per prompt, varying hyperparameters (seed, guidance scale, steps). Because initial user feedback indicated difficulty differentiating images, we applied greedy selection strategy choose the 4 most distinct images based CLIP similarity scores. Algorithm details this procedure.
C.3. Annotation Details
Figure 12 shows the web-based annotation interface. Users rated each image pair by moving a slider to the left right, indicating their preference strength neutrality. They could rate up three randomly assigned criteria per comparison, consulting embedded definitions needed.
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end for LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Arphhtt
Inclusion/ Inclusive
Safe/ Secure
Comfortable
Submit
+
14/750
Figure Annotation interface for LIVS. Users compared two images on slider. optionally clicking on purple next to each criterion for its definition.
Algorithm Selecting the Most Diverse Images Using CLIP Similarity Scores
1: Input: Similarity matrix S (size n x n), number of images k = 4
2: Output: Indices the selected images, iileecte_indicnn
3: n +le((S))
4: selected indices +
5: finst.indez + arg min (mean(S, axis 1))
6: Append finet
selected_indices
7: j (k 1) do
8: min-similarin + 00
9:
+ -
10: for 0 (n - do i & selected indices then current-similariin + sexeS[selected. if current similarity min sinilariml then
i))
13:
14:
min min_similarity +- current_similarin
15:
index t- i 16: end
17: end if
Append next_index indices
20: end for
21: return selected.indices
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
D. LIVS Annotation Protocol
Your help is crucial for creating an Al model that understands what makes Montreal's public spaces good for everyone. This guide will assist you in comprehending the labeling process, how to conduct labeling, and what look for during this exercise.
D.1. Before You Start
Read the criteria definitions: Understand what each criterion (like safety comfort) signifies before you begin. (See the definitions below.)
Take breaks: Avoid attempting to do too many annotations at once. It's preferable to return refreshed.
Take your time: Ensure the annotations are of high quality. On average, each comparison should take hetween 15 to seconds.
Use computer: This task is more manageable on computer than on a phone or tablet.
D.2. The Labeling Process
Evaluating image pairs: You will assess each pair of images based on three specific criteria displayed on the webpage. For each criterion, adjust the slider to indicate the image on the right or left better aligns with that criterion. If neither image fits or both are equally suitable, you may position the slider in the center. However, be aware that center positions provide distinct preference data.
Personal perspective: Annotate based on your own judgment, experience, and perspective. We value your individual insight and are not seeking an objective assessment.
Focus on urban space characteristics: Your decision should be based solely on the characteristics of the urban space rather than the presence of people or animals.
Handling distorted images: Do not spend additional time making sense of disfigured or unclear images, as these are common with AI- Al-produced imagery.
Utilize the commenting feature: This allows you add nuances context to your annotations. Remember, this is voluntary and does not count towards the total number of annotations.
Asking questions: you'le re ever unsure about anything. please send us an email at
umontreal.com
D.3. Labeling Duration
You can label the images web platform accessible from any location. A code will be sent to your email for access.
Simply create profile using your email and chosen password. Below is the schedule for image annotation:
Duration: The labeling spans 8 weeks, organized into four 2-week batches.
Task: Each batch requires the annotation of 750 images.
Start date: The labeling begins on 01 May 2024.
End date: Please try to finish 750 annotations before the deadline for each batch. The deadlines are indicated below.
Please note that the platform limits users to 90 annotations per session, with each session lasting about 25 minutes. Completing all annotations for each batch is estimated take around hours.
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
D.4. Labeling Timeline
First batch: 01 April 14 May
Second batch: 15 May - 28 May
Third batch: 29 May- - 11 June
Fourth batch: 12 June 20 June
D.5. Definitions
Public space: Public space is area where everyone go, like parks and streets, designed for people to meet, play, and relax together in cities and towns.
Inclusion (Inclusive):
Spaces where everyone is welcome and feels respected. These are places that do not discriminate
against anyone.
Safe / Secure:
Spaces where public safety ensured through various measures. Spaces where one feels calm and safe, free from dangers related to physical elements, pollution, or any other concerns that could diminish sense of security.
Comfortable: Well-equipped spaces with quality facilities that provide material comfort; places where one feels at ease and protected from the elements.
Inviting: Spaces that attract and engage people through appealing elements and activities; places that encourage community participation and interaction.
Diverse: Spaces that cater to the diversity of social groups and to the variety of services, activities, and functions.
These are places offering range of uses and meeting the needs of different cultures, ages, and abilities.
Accessibility: Urban spaces that are easily accessible and navigable for everyone, regardless of physical ability. These include features such ramps, wide walkways, clear signage, and tactile indicators for safe and convenient access throughout the area.
E. Prompting Workshop Details
Questions for the Prompt Creation
What the surroundings?
What decorative features and objects does the place have?
Is there nature present, and yes, what kind?
How are the weather and light conditions?
What the composition of the image?
What materials and surfaces are present?
How would you describe the atmosphere?
What amenities should be present in the space?
Questions for the Evaluation of the Images
Can you imagine using the public space yourself?
Does the public space match what you had imagined when creating the prompt?
Are you satisfied with the image?
Can you see the image being used as design for public space in real life?
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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Groups
Group 1
First hands-on session Scenario A
Second hands-on session Scenario
Optional Scenario
Group 2
First hands-on session Scenario B
Second hands-on session Scenario C
Optional Scenario G
Group 3
First hands-on session Scenario C
Second hands-on session Scenario
Optional Scenario
Group 4
First hands-on session Scenario D
Second hands-on session Scenario E
Optional Scenario I
Group 5
First hands-on session Scenario E
Second hands-on session Scenario
Optional Scenario J
Scenarios List
Scenario A
Visualize this public space with provided tools:
Public space typology: Park
Amenities: Sitting space, green space
Location: Less dense suburban Montreal
Scenario B
Visualize this public space with provided tools:
Public space typology: Pedestrian promenades
Amenities: Safe streets, community engagement spaces, green spaces
Location: Historical neighborhood Montreal
23

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Scenario C
Visualize this public space with provided tools:
Public space typology: Street space
Amenities: All ages-, all genders-, all abilities-, all identities-friendly environments
Location: Residential neighborhood in Montreal
Scenario D
Visualize this public space with provided tools:
Public space typology: Downtown plaza
Amenities: Meeting spaces, sitting area, rest areas, versatile use space
Location: Downtown Montreal
Scenario
Visualize this public space with provided tools:
Public space typology: Park
Amenities: Rest areas, community engagement spaces, waterfront area
Location: Dense urban area in Montreal
Optional Scenarios
Scenario F
Visualize this urban space:
Public space typology: Urban garden
Amenities: Educational programs, community gardening spaces
Location: Near universities and colleges in Montreal
Scenario G
Visualize this urban environment:
Public space typology: Waterfront sidewalk
Amenities: Outdoor cafes, installations, pedestrian paths, bike lanes
Location: Along a river or lake in mixed-use (residential and commercial uses) area of Montreal
Scenario H
Visualize this community space:
Public space typology: Neighborhood square
Amenities: Playgrounds, outdoor fitness equipment, community noticeboards, seasonal markets
Location: Residential area in Montreal, possibly near schools and local businesses
24

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Scenario I
Visualize this communal area:
Public space typology: Transit plaza
Amenities: Sheltered seating, transit information displays, public art, bike-share stations
Location: Key transit hub in Montreal, adjacent to a metro station or major bus interchange
Scenario J
Visualize this urban setting:
Public space typology: Alleyway
Amenities: Street murals, pedestrian lighting, small business kiosks
Location: Back alleys commercial district of Montreal
F. Experiment Details
We fine-tuned Stable Diffusion XL using Direct Preference Optimization (DPO; Rafailov et al. 2024), closely following the original hyprrparameters:
Batch Size: 64
Learning Rate: x 10-8 with 20% linear warmup
Beta (3): 5,000
Training Steps: 500 smaller subsets; 1,500 when combining the entire preference dataset
Hardware: Single NVIDIA A100 80GB GPU
All preference values (continuous slider results) were discretized to binary labels (preferred vs. not preferred) for DPO compatibility. While majority-votinn procedure resolved multi-criteria conflicts, future work could explore methods that retain richer preference signals.
G. Qualitative Observations
Figures 13, 14, and 15 compare baseline SDXL images against versions finetuned specific or multiple LIVS criteria. While DPO alignment often improves features such as smooth surfaces or clearer paths, certain elements (e.g., ramps, multilingual signage) appear inconsistently, indicating the model's limited capacity for rendering specialized details.
H. Additional Analysis: Intersectional Scoring
H.1. Case Study IV: Scoring Patterns of SDXL Images by Intersectional Identities
Figures 16 and 17 plot average raw scores from intersectional groups (c.g., disability status X racc/ethnicity) for SDXL- generated images across the six LIVS criteria. Participants with mobility challenges generally assigned lower Accessibility and Safery scores, highlighting the importance of soliciting localized, intersectional feedback in T2I alignment for public- space design.
25

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Prompt: A bike path separated from vehicular traffic by barriers.
SDXL original
SDXL-DPO for safe criterion
SDXL-DPO for all six criteria
Figure 13. Bike Path Scenario Focused on Safety. From left to right: baseline SDXL output with minimal barriers; safety-tuned version stronger separation lacking comfort features; and a multi-criteria DPO version featuring wider lanes smoother transitions, though certain amenities remain missing.
Prompt: shopping mall with wide aisles, ramps, and accessible restrooms.
SDXL original
SDXL-DPO for accessibility criterion
SDXL-DPO for all six criteria
Figure 14. Shopping Mall Scenario Emphasizing Accessibility. From left: baseline SDXL with limited ramps; an accessibility version that adds stairs; and multi-criteria DPO output showing wider walkways but still struggling with ramp clarity.
26

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Prompt: A metro station decorated with artwork representing different heritages.
SDXL original
SDXL-DPO for diversity criterion
SDXL-DPO for all six criteria
Figure 15. Metro Station Scenario Emphasizing Diversity. From left: baseline SDXL with limited demographic variety; diversity- focused model offering modestly varied individuals; and the DPO-tuned version showing slightly broader representation, though cultural markers (e.g., multilingual signs) remain muted.
27

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Figure 16. Main dataset: SDXL-scored public-space images, grouped by intersectional identity. Participants with mobility constraints often assigned lower Accessibility ratings.
28

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Orderee
Acasbbyy
Quseituilyy Viex Now Aan foel Seote
Ciouid hecls Kuak kid, boild
14/0%
1V9%C
Cr
Oltann
Cooor darry 1imal Cran, Ale
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Ciina Insatyy Fonernal Ao riaey
Figure 17. Evaluation dataset: SDXL-scored images, again grouped by intersectional identity. Patterns parallel those observed in the main datasct, with lower Accessibility or Safety scores among some groups
29

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
I. Variance
2000
Number of Responses
1500
1000
500
Favoring DPO
Neutral
Favoring Base
Total
Preference Category
Figure 18. Preference Distribution for DPO vs. Baseline. Each bar represents the share of 2,200 final annotations favoring the DPO- aligned model, the baseline, indicating neutrality. Neutral selections suggest subtle differences or partial fulfillment of criteria by both images.
Inviting
Diverse
Contortable
Ciilerion
Azcessibility
Accessibiltty
Comfortable
Inviting
Inclusive
Neutral Rate
Score Variance
Figure 19. Main dataset: neutral ratings and variance by criterion. Higher variance in some criteria (e.g., Inclusivity) may reflect subjective or diffcuul-to-visuulizz concepts.
30

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LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces
Diverse
Comfortable
Accessibiltty
Inviting
Comfortable
Accessibility
Urttennn
Crrfonen
Inclusive
Safe
Safe
Inclusive
Inviting
Diveree
0.000
0.025
0.050
D.100
0.125
0.150
0.175
0.200
0.000
0.001 0.002
0.003
0.034
0.005
0.006
0.007
0.008
Neutral Rate
Score Variance
Figure 20. Evaluation neutral rating and variance hy criterion. Similar patterns of higher neutrality persist for Inclusivity and Diversity.
31


Reviews:
[{"review_id": "0p7z8Brrop", "reviewer": "Reviewer_8G5x", "summary": "The authors of this paper collected a human preference dataset (called LIVS) of generated images about public spaces. The preference focuses on evaluating six metrics, including Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity. Then, they finetune a Stable Diffusion XL model using Direct Preference Optimization (DPO). The finetuned SDXL model's generated images got more preference than the original SDXL's images (32% vs 14%).", "questions": "", "limitations": "", "rating": 3, "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": "Thank you for the constructive feedback. We clarified the rationale behind using community-informed prompts over universal keywords, explained the three-criteria annotation design, and updated key sections for clarity.\n\n---\n### Comment 1: Necessity of the LIVS Dataset vs. Enhanced Prompting\n**Response:**  \nWe recognize that refined prompting strategies—for instance, appending keywords such as *accessibility*, *safety*, or *inclusivity*—can guide generative models toward more targeted outputs. Approximately half of the prompts used to generate images included at least one criterion (see Table 1). However, LIVS emphasizes that *lived experience* varies considerably across social identities and contexts. In many urban settings, local communities articulate inclusivity or accessibility in ways specific to their sociocultural histories. As demonstrated in prior research (e.g., Beebeejaun, 2017; McAndrews et al., 2023), relying solely on universal keywords can impose a fixed and potentially biased interpretation of inclusive space. By contrast, our dataset incorporates diverse local knowledge and community priorities, reducing the risk of reproducing a single design standard that may neglect intersectional needs (Crenshaw, 1997; Low, 2020).\n\n**Table 1: Prompts with at least one of the six criteria vs. prompts without explicit criteria, and corresponding neutral response performance.**\n\n| Prompt Type      | % of Prompts | % Neutral Responses |\n|------------------|--------------|----------------------|\n| Without Criteria | 51.67%       | 21.76%               |\n| With Criteria    | 48.33%       | 26.26%               |\n\nWe adopted a localized approach without imposing a predefined notion of inclusive space on participants. Our objective was to maximize the diversity of both images and prompts, allowing for an emergent understanding of what *inclusive* or *diverse* space could mean in different contexts (Anttiroiko & De Jong, 2020; Madanipour, 2010). To our understanding, this design encourages community-driven insights rather than a one-size-fits-all approach to inclusivity.\n\n**Revised Paragraph (in *Prompting and Early Feedback*):**  \n*“Although refined prompting techniques can shape generative outputs, universal keywords alone may overlook local sociocultural and historical contexts (Anttiroiko & De Jong, 2020; Beebeejaun, 2017; Talen, 2012). Our goal in creating the LIVS dataset was to integrate granular, community-generated perspectives on accessibility, safety, and inclusivity. By embedding localized knowledge, we reduce the likelihood of producing a uniform design standard that might disregard certain intersectional needs (Low, 2020; Madanipour, 2010; McAndrews et al., 2023).”*\n\n---\n\n### Comment 2: Annotating Only Three of the Six Criteria per Comparison\n\n**Response:**  \nDuring our tutorial workshop, participants indicated that rating fewer or more than three criteria simultaneously was cognitively demanding and reduced their focus on each image. Including more criteria also gave a sense of reduced progress, especially since the initial annotation batches comprised around 1,000 comparisons, later reduced to 750. We also observed that displaying six rating elements constrained the interface, limiting visibility of the image pairs and making it harder to assess spatial details. As a result, we randomly assigned three criteria per annotation task. This approach balanced coverage and participant effort while preserving image clarity. We confirmed that all six criteria received substantial coverage across multiple batches.\n\n**Revised Paragraph (in *Annotations and Evaluation*):**  \n*“To optimize data quality and minimize cognitive fatigue, we randomly presented three of the six criteria in each comparison. During pilot trials, participants found evaluating all six criteria difficult, which reduced their sense of progress and visual engagement. The multiple rating elements also constrained image size, making it harder to assess spatial details. Focusing on three criteria enabled more meaningful engagement. Over successive annotation batches, we ensured that all six criteria were robustly evaluated.”*\n\n---\n\n**We hope these clarifications and revisions address Reviewer's concerns. We appreciate their detailed comments, which have helped strengthen the methodological clarity and contextual framing of the paper.**\n\n---\n\n**References**  \n1. Anttiroiko, A.-V., & De Jong, M. (2020). *The Inclusive City*. Springer.  \n2. Beebeejaun, Y. (2017). Gender, urban space, and everyday life. *J. Urban Affairs, 39*(3).\n3. Crenshaw, K. (1997). Demarginalizing intersectionality. In *Feminist Legal Theories*. Routledge.  \n4. Low, S. (2020). Social justice and public space. In *Companion to Public Space*. Routledge.  \n5. Madanipour, A. (2010). *Whose Public Space?* Routledge.  \n6. McAndrews, C. et al. (2023). Toward gender-inclusive streets. *J. Planning Lit., 38*(1).\n7. Talen (2012). *Design for Diversity*. Routledge.", "claims_and_evidence": "- The authors claim that the LIVS dataset captures diverse, community generated dimensions of inclusive public space design. I think this claim is clearly supported by the data collection process, where communities participated in the workshops and condensed the evaluation metrics into six aspects. \n- The authors showed the effectiveness of using the collected LIVS dataset (via DPO) to finetune SDXL towards human preference aligned with the six metrics. This claim is supported by the human evaluation experiment to compare the finetuned SDXL and the original one (Section 4.1). \n- The authors suggested that their results show the influence of participant identities on model preferences and the difference of the generated images resulted from human-authored and AI-generated prompts. These claims are supported by Figure 7 and Figure 8, respetively.", "methods_and_evaluation_criteria": "The data collection is professional, where detailed instructions were given in a series of workshops and the annotators had sufficient understanding of the topic about public space design. The DPO is properly used to finetune SDXL with the collected preference data. Finally, it is correct to use human evaluation to check if the finetuned SDXL achieves better results than the original SDXL.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The experimental analyses are mainly based on statistical analysis (Figures 4, 5, 7, 8), which show the distributions of ratings. This is the correct way to do the analysis.", "relation_to_broader_scientific_literature": "The collected LIVS dataset would be useful in the community conerning public space design.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "I want to raise a fundamental question regarding the necessity of developing the LIVS dataset to improve the image generation models' capacity in generating images that align with the six aspects in designing public spaces. I think it might improve the images better by specifying a better, more detailed prompt. A simple prompting method is by appending the six criteria to the prompts. An example can be \"a shopping mall with wide aisles, ramps, and accessible restrooms. It should follow these criteria: Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity.\" How does this strategy improve the generated images?", "other_comments_or_suggestions": "No.", "questions_for_authors": "Why were only three randomly selected criteria from the total of six criteria shown in each annotation? Why not use all six criteria? Since the annotators have taken time to check the images, it would not take much more time to finish the evaluation on the remaining three criteria. Evaluating on all six criteria could results in more data with high efficiency.", "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": "", "extra_scores": {}}, {"review_id": "XTvTqdHfXb", "reviewer": "Reviewer_adw2", "summary": "The authors contribute LIVS, a benchmark for aligning text-to-image (T2I) models with respect to multiple criteria (Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity) in the context of urban public space design. The benchmark was developed via two-year participatory process with 30 community organizations in Montreal. The authors use DPO with 35,510 multi-criteria community preference annotations to align a Stable Diffusion XL model and find that: (1) the resultant generations can be better aligned with the preferences, (2) there remain a significant amount of neural ratings of the generations (possibly due to the complexity of modeling intersectional preferences), and (3) larger-scale alignment can be more effective. The authors also study the effect of prompt variation on community ratings of generations (observing that human-authored prompts are better at eliciting decisive preferences than synthetic prompts), and find that preferences vary across identities.\n\n## Update after rebuttal\n\nThe authors provided a detailed response and suggested beneficial revisions based on my comments. I have maintained my (already high) score.", "questions": "", "limitations": "", "rating": 4, "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": "We thank the reviewer for their detailed and constructive feedback. Below, we respond to each comment. Where revisions are needed, we provide the updated text.\n\n---\n\n### Comment 1: Multi-criteria preference learning but collapsing annotations, neutral annotations not leveraged; disagreements across criteria are collapsed\n\n**Response:**  \n\nWe acknowledge that our current method for DPO training reduces multi-criteria signals into a single reward label. While this dataset is inherently multi-criteria, this reduction reflects a pragmatic simplification for the initial phase of DPO training. However, this also underscores a broader limitation of DPO: it is not straightforward to perform multi-criteria alignment—particularly when preferences across criteria conflict or when alignment must be criteria-aware within the same model. We have clarified this in the revised text and emphasize that future work is needed to develop methods capable of capturing such partial, intersecting, or contradictory preferences in a more principled way.\n\n**Revision in 2.1. Alignment of Generative Models:**  \n*“Although the LIVS dataset contains multi-criteria feedback, we initially collapse these signals into a single preference label for each pair during DPO. This step overlooks conflicting or nuanced assessments across different criteria. Future work is needed to explore approaches that account for intersections and disagreements without forcing a single binary label, thereby preserving the richness of multi-criteria preference data.”*\n\n---\n\n### Comment 2: Figure 3 does not clearly show \"comprehensiveness\" of prompts\n\n**Response:**  \nWe revised the text to clarify that the word cloud is a preliminary visualization of different concepts within prompts. We rely on Jensen–Shannon Divergence (JSD) scores and scenario-based coverage to demonstrate prompt diversity. The figure is meant only as an illustrative snapshot.\n\n---\n\n### Comment 3: Synthetic prompts not validated by humans\n\n**Response:**  \nWe acknowledge that synthetic prompts were primarily evaluated using automatic diversity checks (JSD). As noted in the limitations, this expands coverage but may lack the contextual specificity of human-authored prompts. Future work needs to incorporate human validation to improve relevance.\n\n---\n\n### Comment 4: Use of \"democratizing\" (line 43) requires more context\n\n**Response:**  \nWe removed the term “democratizing” and clarified that T2I tools aim to lower barriers to community participation in design. Thank you for the reference.\n\n**Revised Sentence in Introduction:**  \n*“These developments can benefit communities by making design processes more accessible—enabling broader engagement among non-expert stakeholders in architecture, urban planning, and environmental visualization.”*\n\n---\n\n### Comment 5: Citation issue\n\n**Response:**  \nRectified.\n\n---\n\n### Comment 6: Participatory action research grounding\n\n**Response:**  \nWe added the following paragraph in the `Methodology: Building the LIVS Dataset` section.\n\n**Revised Paragraph:**  \n*“Participatory Action Research (PAR). In line with the principles of PAR, our community-based approach centers on iterative, collaborative inquiry and reciprocal learning throughout the dataset development process (Israel et al., 1998; Cornish et al., 2023). By involving local organizations as active co-researchers, we ensured that the framing of inclusion, safety, and other design criteria emerged from lived experiences rather than external prescriptions. This iterative feedback loop aligns with PAR’s emphasis on collective problem-solving and empowerment, as participants guided each stage of data collection and model evaluation while gaining familiarity with T2I technology and its potential applications in urban contexts.”*\n\n---\n\n### Comment 7: Question—On average, how many criteria per image pair?\n\n**Response:**  \nOn average, each image pair received approximately 1.49 non-zero (decisive) annotations from community members.\n\n---\n\n### Comment 8: Question—Refining definitions like \"Inclusivity\" for more distinct preferences\n\n**Response:**  \nThis is a trade-off: while detailed definitions may yield clearer preferences, our prompts were prescriptive and annotations observational to avoid biasing participants. Now, with the full dataset, we plan to analyze each criterion by identifying subdimensions and associated objects for emerging patterns. \n\n---\n\n### Comment 9: Question—Figure 8: Do left and right images come from the same or different models?\n\n**Response:**  \nRegarding Figure 9 (since Figure 8 pertains to prompt methods), both images were generated by the same SDXL model.\n\n---\n**We appreciate the suggestions, which helped clarify multi-criteria signals, prompt diversity, and neutral annotations. We believe the revisions address the reviewer’s concerns and strengthen the paper.**\n\n**References** \n1. Israel, B. A. et al. (1998). Annu. Rev. Public Health\n2. Cornish, F. et al. (2023). Nat. Rev. Methods Primers", "claims_and_evidence": "- The claims are generally supported by clear and convincing evidence.\n\n- Lines 76-77: The authors claim that their \"approach applies multi-criteria preference learning,\" but they ultimately collapse the multi-criteria preference annotations into a single binary annotation via majority aggregation. \n\n- Figure 3 does not provide clear evidence for the \"comprehensiveness\" of the prompt dataset (line 262).", "methods_and_evaluation_criteria": "- The authors facilitated an extensive participatory design process over two years with 30 community organizations, consisting of public education, 11 workshops, 34 interviews, and inclusive data collection. This methodology is excellent, as it positions community members as co-creators of LIVS, from criteria design to data annotation, and captures complex and diverse local preferences.\n\n- The authors augment human-collected prompts with synthetic prompts generated using GPT-4o, but the synthetic prompts are not validated by humans (only validated automatically). The authors note in line 370 that the synthetic prompts likely lack \"contextual specificity.\"", "theoretical_claims": "The authors did not make any theoretical claims.", "experimental_designs_or_analyses": "- To perform preference learning, the authors collapse the multi-criteria preference annotations into a single binary annotation via majority aggregation, which does not preserve differing intersectional preferences.\n\n- The resultant generations from the aligned SDXL model are compared to generations from the baseline SDXL model on a held-out set of prompts, which is sound.", "relation_to_broader_scientific_literature": "- The authors thoroughly explore the relationship between their work and existing work on the alignment of generative models, intersectionality, visual generative modeling for urban spaces, and multi-criteria preference learning.\n\n- The authors go beyond much prior work on global/universal alignment by capturing local multi-criteria preference annotations, specifically in the context of urban public space design.\n\n- The authors go beyond the common conceptualization of Intersectionality as merely overlapping social groups [1] and discuss how overlapping forms of marginalizations affect people's preferences about, e.g., accessibility (Section 2.2).\n\n- The authors build on the tradition of meeting community design objectives in urban planning.\n\n- The paper is similar to prior work on multi-criteria preference learning, but does so by aligning SDXL with respect to multiple criteria (Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity) in the context of urban design.\n\n- Some citations may not directly support the authors' claims, e.g., [2] in lines 115-119. The authors should double-check that all their parenthetical citations are directly relevant.\n\n[1] https://dl.acm.org/doi/10.1145/3600211.3604705\n\n[2] https://dl.acm.org/doi/10.1145/3613904.3642703", "essential_references_not_discussed": "I am not aware of any essential references that were not discussed.", "other_strengths_and_weaknesses": "Strengths:\n- The authors focus on aligning models with local community preferences around inclusive urban planning, thereby advancing pluralistic alignment.\n\n- The authors transparently document their ethics and inclusivity considerations, e.g., compensating community members, utilizing an inclusive and accessible data annotation interface.\n\n- The paper is clearly written and well-organized.\n\n- The authors offer numerous directions for future work, e.g., leveraging neutral annotations as a signal, disentangling ratings for overlapping criteria.\n\nWeaknesses:\n- The authors do not explicitly leverage neutral annotations or disagreements in annotations across criteria during preference learning.", "other_comments_or_suggestions": "- Minor comment: The use of \"democratizing\" (line 43) requires further contextualization in the paper. In particular, the use of \"democratizing\" might be a bit misleading given that the paper does not discuss, e.g., democratic governance structures for T2I models in urban planning [1].\n\n- The authors should expand on how their work relates to participatory action research.\n\n[1] https://aclanthology.org/2024.emnlp-main.184/", "questions_for_authors": "- On average, how many criteria did a community member annotate per image pair?\n\n- How may the definition of criteria like Inclusivity be refined to lead to more distinct preferences? Should the definition be more prescriptive or descriptive?\n\n- In Figure 8, do the left and right images in a single pair come from the same or different models?", "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": "", "extra_scores": {}}, {"review_id": "IfkEEm9dwq", "reviewer": "Reviewer_ABdq", "summary": "This paper introduces a new dataset LIVS, which encodes community-generated plurastic preference data towards text-to-image for urban planning. This dataset is is built from data collected from 30 community organizations to develop a framework of 6 axes along which urban public space design can be evaluated. Based on this framework, the authors collect the dataset of 38k human preference annotations.\n\nThe training split of this data is used to perform DPO finetuning on Stable Diffusion XL, which is then tested on the validation split for analysis. The authors find that the trained model has a considerable win rate over the baseline but also that half of the matchups have a neutral outcome, which is presumed to illustrate the subjective and plural nature of the criteria.", "questions": "", "limitations": "", "rating": 4, "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": "We thank the reviewer for their feedback. We clarified how criteria were defined, explained neutral judgments, revised Figure 1, and added detail on axis-specific outcomes and image generation settings.\n\n---\n\n### Question 1: Axes Relevance and Plausibility\n\n**Response:**  \nIt was both, since participants were looking at images during the focus groups. We revised our Criteria Consolidation section to clarify that the final six criteria emerged from community input on design and from probing whether each dimension could be assessed through images.\n\n---\n\n### Question 2: Neutral Judgments and Multi-Criteria Evaluation\n\n**Response:**  \nWe clarify that neutral judgments reflect both genuinely balanced preferences and difficulties in visually encoding certain criteria, especially Inclusivity and Diversity.\n\n**New text in first paragraph in \"Neutral Annotations as a Signal\":**  \n*“Approximately half of the final evaluations were rated as neutral. While these outcomes may initially seem to indicate a lack of meaningful improvement, they could also stem from genuinely balanced preferences and participants’ difficulty in identifying visual cues for certain criteria (especially Inclusivity and Diversity). In several interviews, participants noted that subtle or symbolic elements did not always appear clearly. This indicates an opportunity to incorporate neutral signals explicitly into training and to explore methods that better visualize intangible attributes, such as inclusive design features, beyond purely aesthetic details.”*\n\n---\n\n### Question 3: Guidance Scale and Subtle Differences\n\n**Response:**  \nYes, we employed a moderate guidance scale during the image generation process. To ensure meaningful variation in outputs, we conducted pilot runs and fine-tuned hyperparameters such as seed, guidance scale, and steps. We also generated 20 images per prompt and used a greedy selection strategy, choosing the 4 most distinct images based on CLIP similarity scores, as detailed in Algorithm 1. This approach helped balance the risk of generating images with either overly subtle differences or excessively stylized divergences. We provide further clarification in Appendix (C.2. Image Generation).\n\n---\n\n### Comment 1: Figure 1 Organization\n\n**Response:**  \nWe have revised the figure (Reviewer iv23 also finds this problematic).\n\n---\n\n### Comment 2: More Nuanced Breakdown per Axis\n\n**Response:**  \nWe include additional discussion of axis-specific outcomes (see *New Paragraph below*), showing that some axes (Comfort, Invitingness) benefit more noticeably from DPO with more annotated examples.\n\n**New Paragraph in \"4.1 Additional Observations\":**  \n*“We further analyzed alignment improvements on each criterion by correlating annotation counts with the DPO model’s win rate. The criteria that received more annotations (Comfort and Invitingness) exhibited stronger improvements, suggesting that denser feedback can refine criteria-specific features more effectively. In contrast, Inclusivity and Safety showed a higher proportion of neutral or mixed outcomes, possibly reflecting both fewer annotations and the inherent difficulty of visually conveying representational aspects through T2I alone.”*\n\n**Please see the link below, which contains a figure on the Average Tendency Toward DPO by Criterion** \nhttps://anonymous.4open.science/r/livs-6E96/average-tendency.png\n\n---\n\n**We hope these revisions address the reviewer’s concerns and enhance the clarity of our manuscript. Thank you for the valuable insights.**", "claims_and_evidence": "The authors claim that DPO taking into account multi-criteria feedback improves image generation in their considered space of urban planning. This claim is supported by their evidence (e.g. 70% win rate among non-neutral judgments). However, the extent of the claim is made weaker by the dominance of neutral judgments, accounting for more than half of the results. Given that collecting more training data to strengthen the claim may be costly due to the rigor involved, it might have been helpful to see a more nuanced breakdown using the existing training data: for example, by showing relative effects on different axes of evaluation when prioritizing those axes during DPO training.\n\nThe authors also claim that the significance of neutral judgments is that they highlight where \"preferences are balanced or where further refinement is needed to accommodate complex intersectional needs\". However, it is possible that a large proportion of neutral judgments also highlight axes of evaluation which may not be picked up by the participants comparing images. Particularly from the proportions displayed in Figure 5, as well as the description of late-joining participants in S4.2, it is possible that some of the axes (Diverse, Inclusive, Safe) may be too difficult or impossible for people to discern from concept art images.", "methods_and_evaluation_criteria": "The authors follow a meticulous and community-involved process to develop their framework of evaluation along 6 axes, and to collect training and testing data to illustrate the application of T2I in the urban planning space. Overall, this set of evaluations and analyses are well-defined and executed.\n\nHowever, from their observations, the authors suggest that \"less involvement in the knowledge-exchange process can lead to different or less pronounced alignment perceptions\". For the purposes of expanding the scope of the study and data, it might have been helpful to expand upon definitions of the chosen concepts to allow more participants to offer their input and preference feedback with lower involvement. For instance, in addition to the short descriptions in S3.2, it would have been helpful to have compiled example images that illustrate the concepts being tested.", "theoretical_claims": "No theoretical claims.", "experimental_designs_or_analyses": "Overall, the experimental designs and analyses appear well thought out. The authors explore multiple aspects of their results, including the effects of identity, intersectional interactions, and prompt compositions.", "relation_to_broader_scientific_literature": "The paper explores the role of T2I models in urban planning from a perspective of pluralistic values. While the paper does not contribute to the pluralistic theory itself, it uses it as a framework to develop a set of criteria with a human-centric method that may be useful in other scenarios as well. The work relates to a lot of prior work in finetuning T2I models for human preferences, but again, focuses on the local and community-centric nature of preferences, and uses them to present more convincing findings.", "essential_references_not_discussed": "None that I am aware of.", "other_strengths_and_weaknesses": "The paper presents the results of a very methodical and well-thought out approach to gathering real preference data from communities, targeting real issues deemed important by those communities. Overall, the paper is original and clear, though its significance is weakened at points due to the weakness of the overall results, which I address in other sections.", "other_comments_or_suggestions": "Figure 1 is organized in an unintuitive way. The y axis shows age group, and it appears that all participants were assigned to one of four age groups, but these appear in no particular order. Why are the datapoints not sorted by age along the y axis?", "questions_for_authors": "The authors conclude that the reason for a large number of \"neutral\" annotations is the highly \"subjective and plural nature\" of determining alignment to people's criteria. However, to me, this appears underjustified (Q1 and 2).\n1. At what point in the process, i.e. Workshops and Interviews, do you consider the relevance of the proposed axes of evaluation in concept art images? Is the determination of the 6 axes driven solely by holistic participant experiences and suggestions, or also by the plausibility of distinguishing those axes from an image?\n2. Similarly, it is suggested that the 1100 neutral rankings during evaluation are due to pluralistic evaluation. Is this statement supported by other evidence, or is it possible that the models differ very little (in generation for particular prompts) or participants find it difficult to measure an image along particular axes?\n3. From the example images and results, it appears that many of the generated images have only very subtle differences after DPO finetuning. Was the conditional guidance scale tuned during generation to ensure that annotators would be able to distinguish key differences between images from the same prompt?", "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": "", "extra_scores": {}}, {"review_id": "fklbEuBdp4", "reviewer": "Reviewer_iv23", "summary": "The paper presents the Local Intersectional Visual Spaces (LIVS) dataset, a community-driven benchmark designed to align text-to-image (T2I) models with intersectional criteria for inclusive urban design. Through a two-year collaboration involving 30 community organizations, the authors iteratively refined 634 initial design concepts into six core criteria (Accessibility, Safety, Comfort, Invitingness, Inclusivity, Diversity) using participatory workshops and 37,710 pairwise comparisons. By applying Direct Preference Optimization (DPO) to fine-tune Stable Diffusion XL (SDXL), they demonstrate improved alignment with these criteria.", "questions": "", "limitations": "", "rating": 2, "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": "We greatly appreciate the reviewer’s feedback. In response, we have clarified our methodological framing of pluralistic alignment, explicitly acknowledged the limitations regarding global metrics such as CLIP and FID, and revised Figure 1 for consistency and clarity.\n\n---\n### 1: The claimed pluralistic alignment lacks support, relying on binary DPO without modeling intersectionality.\n\n**Response:**  \nWe propose a pluralistic alignment framework, as our dataset and workflow explicitly incorporate multiple criteria—Accessibility, Safety, Comfort, Invitingness, Inclusivity, and Diversity—each annotated independently. While we apply DPO, our primary aim is to test its capacity to capture community-driven, intersecting needs. The core contribution of this work lies not only in the dataset itself but in the participatory framework through which it was created. Our originality lies in operationalizing pluralistic alignment: from co-developing multi-criteria definitions with communities, to collecting fine-grained preference data, to empirically fine-tuning a T2I model. We further demonstrate how DPO can be extended to reflect localized values. Models fine-tuned on LIVS and other datasets developed using this framework have potential applications in community consultations and urban design processes. We hope the reviewer recognizes that this approach meaningfully advances alignment research by grounding it in real-world civic contexts. Our human preference data offer greater relevance for intersectional, community-based alignment than generic image quality metrics. The high proportion of neutral judgments, in particular, underscores the complexity of socio-spatial values—dimensions that standard T2I evaluation metrics are ill-equipped to measure.\n\nTo further clarify this distinction, we have revised the relevant text in the Introduction section as follows.\n\n**Revised text in Introduction:**  \n*“We propose a participatory data-collection framework that captures intersectional, multi-criteria feedback for T2I models in inclusive public-space contexts. While we refer to this as a *pluralistic alignment* approach to emphasize the local diversity of preferences, our method currently employs standard DPO with binary preference pairs, rather than a specialized multi-objective optimization algorithm. By integrating multiple locally defined criteria into preference annotations, we aim to expose both the potential and the limitations of a single-objective approach in accommodating intersectional needs.”*\n\n---\n### 2: No CLIP or FID; relies on user qualitative analysis\n\n**Response:**  \nWe intentionally foreground community-derived judgments over global image-similarity metrics, as our primary aim is to assess how well the generated images align with local, context-specific criteria. FID, measures similarity to a global real-image distribution and is not meaningful in our context. We also avoid CLIP-based metrics, as prior work ([1], [2]) shows weak correlation with human judgments. Given our focus on subtle, value-driven distinctions across communities, relying on CLIP risks missing the very nuances we aim to evaluate. \n\nNonetheless, we acknowledge that such metrics can provide useful insights into broader questions—such as identifying common features shared by inclusive spaces across different typologies. \n\n**To reflect CLIP and FID limitations, we added this to the Limitations section:**   \n\n*“Our evaluation centers on human preference judgments rather than traditional metrics such as CLIP scores. While these metrics are useful for quantifying generative fidelity, they fall short in capturing nuanced, local, or intersectional considerations. ”* \n\n---\n### 3: Figure 1 problem\n\n**Response:**  \nRevised Figure 1 for clarity and consistency.\n\n---\n### 4: More project than a novel research contribution.\n\n**Response:**  \nWe respectfully disagree. Our framework and dataset are novel on multiple levels: (i) they capture multi-criteria feedback from diverse communities, thereby reflecting nuanced, real-world norms; (ii) they empirically demonstrate how DPO performs on intersectional needs, exposing a key gap in current alignment methods; and (iii) they introduce a participatory data collection framework that systematically grounds alignment in heterogeneous civic contexts. Unlike ImageReward and other prior efforts that rely on homogeneous annotator pools, our pluralistic dataset and analysis illuminate both the potential and the limitations of single-objective alignment when values meaningfully differ across populations. This combination of empirical insights and community-based methodology advances alignment research beyond conventional quality metrics, underscoring its broader social implications.\n\n---\n\n**References:**\n\n[1] Ku et.al. VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation. ACL 2024\n\n[2] Ku et.al. ImagenHub: Standardizing the evaluation of conditional image generation models. ICLR 2024", "claims_and_evidence": "Supported Claim: DPO enhances alignment for criteria with sufficient annotation support, as evidenced by the user study in Case Study I and Figures 6, 12–14.\n\nUnsupported Claim: While the authors assert that their work proposes a pluralistic alignment framework for T2I models, this claim lacks justification. Since DPO's training signals remain confined to binary preference pairs and do not explicitly model intersectional interactions between criteria, no true multi-criteria framework is established. Notably, neither algorithmic adaptations nor multi-objective optimization strategies are introduced.", "methods_and_evaluation_criteria": "The participatory methodology for dataset construction (e.g., workshops, iterative concept refinement, pairwise comparisons) is clearly articulated and contextually appropriate for capturing community-driven priorities. However, the technical alignment approach relies entirely on off-the-shelf DPO without innovations addressing multi-criteria challenges. Evaluations include user studies and qualitative analyses, but standard quantitative metrics for T2I alignment (e.g., CLIP scores, FID) are notably absent.", "theoretical_claims": "No theoretical claims or formal proofs are presented.", "experimental_designs_or_analyses": "Experiments center on four user study-based case analyses: Case Study I demonstrates DPO's effectiveness for high-data criteria; identity-driven analyses (Cases II/IV) reveal preference variations across demographics; Case III underscores the critical role of prompt design in evaluation reliability. A reliance on qualitative outcomes persists, with quantitative metrics omitted.", "relation_to_broader_scientific_literature": "This work is related to T2I alignment methods relying on global metrics (ImageReward, HPS) and multi-criteria preference learning (MPS). It extends these by grounding multi-attribute annotations and local urban design contexts.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "None.", "other_comments_or_suggestions": "The paper’s main contribution is its dataset construction, which is well explained and offers useful insights for future work. However, the claimed pluralistic alignment framework is unclear, as the DPO algorithm remains binary and only the annotations are multi-criteria—a concept already explored in prior works (e.g., ImageReward). Overall, this work is more of an excellent project than a novel research contribution. I am willing to raise my score if the authors can address my concerns.", "questions_for_authors": "Figure 1's caption references the inclusion of age, gender, race/ethnicity, and disability demographics, yet only age distributions are visually presented. Where are other demographic breakdowns (e.g., gender ratios) reported or illustrated?", "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": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: Reviewers are generally (though not unanimously) very positive about this paper, which presents a dataset for urban space design generated in a 2-year participatory design process with 30 community organizations. In authors’ words from the rebuttal, they “…apply DPO …to test its capacity to capture community-driven, intersecting needs. The core contribution of this work lies not only in the dataset itself but in the participatory framework through which it was created. Our originality lies in operationalizing pluralistic alignment: from co-developing multi-criteria definitions with communities, to collecting fine-grained preference data, to empirically fine-tuning a T2I model”

A few suggestions came up during rebuttal that I thought would be worth further consideration in the final version:
- recommendation to add quantitative image similarity analysis, possibly just to show that it does not align with community preferences
- while the rebuttal point that this approach maximizes diversity over a single strategy of adding keywords is well taken, more discussion to clarify this point in the final version may help
Decision: Accept (poster)