Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909694662017024 |
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| author | Rastogi, Charvi Teh, Tian Huey Mishra, Pushkar Patel, Roma Wang, Ding Díaz, Mark Parrish, Alicia Davani, Aida Mostafazadeh Ashwood, Zoe Paganini, Michela Prabhakaran, Vinodkumar Rieser, Verena Aroyo, Lora |
| author_facet | Rastogi, Charvi Teh, Tian Huey Mishra, Pushkar Patel, Roma Wang, Ding Díaz, Mark Parrish, Alicia Davani, Aida Mostafazadeh Ashwood, Zoe Paganini, Michela Prabhakaran, Vinodkumar Rieser, Verena Aroyo, Lora |
| contents | Current text-to-image (T2I) models often fail to account for diverse human experiences, leading to misaligned systems. We advocate for pluralistic alignment, where an AI understands and is steerable towards diverse, and often conflicting, human values. Our work provides three core contributions to achieve this in T2I models. First, we introduce a novel dataset for Diverse Intersectional Visual Evaluation (DIVE) -- the first multimodal dataset for pluralistic alignment. It enable deep alignment to diverse safety perspectives through a large pool of demographically intersectional human raters who provided extensive feedback across 1000 prompts, with high replication, capturing nuanced safety perceptions. Second, we empirically confirm demographics as a crucial proxy for diverse viewpoints in this domain, revealing significant, context-dependent differences in harm perception that diverge from conventional evaluations. Finally, we discuss implications for building aligned T2I models, including efficient data collection strategies, LLM judgment capabilities, and model steerability towards diverse perspectives. This research offers foundational tools for more equitable and aligned T2I systems. Content Warning: The paper includes sensitive content that may be harmful. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models Rastogi, Charvi Teh, Tian Huey Mishra, Pushkar Patel, Roma Wang, Ding Díaz, Mark Parrish, Alicia Davani, Aida Mostafazadeh Ashwood, Zoe Paganini, Michela Prabhakaran, Vinodkumar Rieser, Verena Aroyo, Lora Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Current text-to-image (T2I) models often fail to account for diverse human experiences, leading to misaligned systems. We advocate for pluralistic alignment, where an AI understands and is steerable towards diverse, and often conflicting, human values. Our work provides three core contributions to achieve this in T2I models. First, we introduce a novel dataset for Diverse Intersectional Visual Evaluation (DIVE) -- the first multimodal dataset for pluralistic alignment. It enable deep alignment to diverse safety perspectives through a large pool of demographically intersectional human raters who provided extensive feedback across 1000 prompts, with high replication, capturing nuanced safety perceptions. Second, we empirically confirm demographics as a crucial proxy for diverse viewpoints in this domain, revealing significant, context-dependent differences in harm perception that diverge from conventional evaluations. Finally, we discuss implications for building aligned T2I models, including efficient data collection strategies, LLM judgment capabilities, and model steerability towards diverse perspectives. This research offers foundational tools for more equitable and aligned T2I systems. Content Warning: The paper includes sensitive content that may be harmful. |
| title | Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.13383 |