Tag-First: Mitigating Distributional Bias in Synthetic User Profiles through Controlled Attribute Generation (2025)

Abstract: Addressing the critical need for robust bias testing in AI systems, current methods often rely on overly simplistic or rigid persona templates, limiting the depth and realism of fairness evaluations. We introduce a novel framework and an associated tool designed to generate high-quality, diverse, and configurable personas specifically for nuanced bias assessment. Our core innovation lies in a two-stage process: first, generating structured persona tags based solely on user-defined configurations (specified manually or via an included agent tool), ensuring attribute distributions are controlled and crucially, are not skewed by an LLM’s inherent biases regarding attribute correlations during the selection phase. Second, transforming these controlled tags into various realistic outputs—including natural language descriptions, CVs, or profiles—suitable for diverse bias testing scenarios. This tag-centric approach preserves ground-truth attributes for analyzing correlations and biases within the generated population and downstream AI applications. We demonstrate the system’s efficacy by generating and validating 1,000 personas, analyzing both the adherence of natural language descriptions to the source tags and the potential biases introduced by the LLM during the transformation step. The provided dataset, including both generated personas and their source tags, enables detailed analysis. This work offers a significant step towards more reliable, controllable, and representative fairness testing in AI development.

## introduction the imperative to ensure fairness and mitigate harmful biases in artificial intelligence (ai) systems is paramount @xcite , especially given their increasing deployment in high-stakes domains such as conversational agents, recommendation systems, and social modeling tasks. however, progress is frequently hindered by significant limitations in existing evaluation methodologies, particularly in how synthetic populations or personas are generated for bias testing. current persona generation approaches face significant hurdles for robust bias testing. manual creation, while potentially rich, is hampered by scalability constraints, cost, and the risk of implicit creator bias @xcite . automated methods introduce their own set of challenges. some rely on rigid templates that can produce stereotypical outputs @xcite . more fundamentally, the evaluation benchmarks used to validate systems are often demographically skewed, which can hide critical performance gaps. the landmark "gender shades" study, for instance, audited commercial facial analysis systems and found substantial accuracy disparities across intersectional subgroups @xcite . the systems performed worst on darker-skinned females (with error rates up to 34.7%) compared to lighter-skinned males (with a max error rate of 0.8%), a disparity linked to the underrepresentation of darkerskinned women in the popular training and benchmark datasets @xcite . this highlights a critical flaw in ai evaluation: without balanced and representative test sets, harmful algorithmic biases can go undetected. this problem of bias extends beyond evaluation data and is deeply embedded in the training corpora of generative models themselves. foundational work by @xcite revealed this danger in word embeddings, showing that models trained on large text corpora absorb and reproduce stark gender stereotypes. this leads to harmful associations like "man is to computer programmer as woman is to homemaker" instead of neutral relationships (e.g., "doctor" being equally related to "man" and "woman") or biologically grounded ones (e.g., "man is to father as woman is to mother"). this issue of bias amplification is even more pronounced in modern large language models (llms). directly using llms for end-to-end persona generation risks magnifying the societal biases present in their training data @xcite and provides little fine-grained control over attribute distributions @xcite . furthermore, the immense scale and opacity of the datasets used to train these models create significant challenges for transparency and validation, a gap that has prompted calls for standardized documentation practices like datasheets for datasets @xcite . these collective limitations underscore the need for a more flexible, controllable, and transparent methodology. to address these limitations, we propose a novel framework centered around a tag-first generation methodology designed for creating flexible, realistic, and statistically controlled personas for rigorous bias testing. this framework tackles the core issue of uncontrolled attribute correlation bias inherent in direct llm generation. the process involves two primary stages: this tag-centric approach offers significant advantages beyond the controlled attribute assignment achieved in stage 1. it provides transparency regarding the exact attributes assigned to each persona, and the persistent tags serve as ground truth. this enables systematic analysis of how generated content correlates with specific attributes and how downstream ai systems respond to these controlled variations. to facilitate the potentially complex task of creating the initial configuration (stage 1), we have developed an interactive tool featuring a conversational agent. this tool guides users, including nonexperts, through the process of defining persona attributes and distributions using natural language dialogue. it assists in creating the necessary structured configuration file, incorporating configurable attribute randomization and offering suggestions informed by the user's specified testing context. manual creation or modification of the configuration file remains possible for expert users. ## related work our work intersects with several research areas: persona generation methodologies, the study and mitigation of bias in large language models (llms), the use of personas for evaluating ai systems, and the inherent challenges of bias in manual processes. ## methodology our persona generation framework operationalizes the tag-first methodology introduced in section 1 (illustrated in figure 1 ). the process is orchestrated through several key components designed for flexibility and control over persona attributes. central to the framework is a structured yaml configuration file that defines the desired attributes and their distributions. an optional agent tool assists users in creating this configuration. based solely on the yaml specifications, the system first generates structured persona tags, which then serve as controlled input for subsequent transformation into richer outputs like natural language descriptions. this section details these components, starting with the configuration structure. 3.1 structured persona configuration (yaml) ## validation and analysis using the finalized yaml configuration (created manually or via the agent), we generated a dataset of 1,000 personas following a systematic, multistep approach designed to ensure both adherence to the configuration and internal consistency. start ask use case select features (llm) are features valid? handle feature errors generate feature insights more features left? adapt feature config show config explain config user feedback show help update config save final config end invalid valid pending done continue help update figure 2: workflow diagram of the interactive agent for yaml configuration generation. diamonds represent decision points based on state or user input. persona tags generation: first, for each persona, the system generates a set of structured tags by sampling values for each attribute according to the probabilities, ranges, and constraints defined in the yaml configuration. this critical step ensures the resulting tag distributions align strictly with the user's specifications before any llm generation occurs. an example tag set for a single persona might be: gender: female race: hispanic past_religion: agnostic current_religion: none socio_economic_status: high born_location_country: spain current_location_world_region: africa political_orientation: conservative disability: none age: 22 sexual_orientation: heterosexual job_title: research assistant first_hobby: sailing studied: psychology validation and transformation pipeline: the generated tags then proceed through a validation and transformation pipeline: this systematic process is designed to yield personas whose underlying attributes are known and controlled. adjective extraction for bias analysis: finally, for the validated personas that passed the adherence checks, adjectives were automatically extracted from their natural language descriptions. this step provides structured data that can be used for subsequent quantitative analysis, particularly for preliminary checks on potential biases or stereotypical language patterns introduced by the llm during the transformation (natural language generation) stage.