"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
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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_ | 1866913952265404416 |
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| author | Wang, Ding Díaz, Mark Rastogi, Charvi Davani, Aida Prabhakaran, Vinodkumar Mishra, Pushkar Patel, Roma Parrish, Alicia Ashwood, Zoe Paganini, Michela Teh, Tian Huey Rieser, Verena Aroyo, Lora |
| author_facet | Wang, Ding Díaz, Mark Rastogi, Charvi Davani, Aida Prabhakaran, Vinodkumar Mishra, Pushkar Patel, Roma Parrish, Alicia Ashwood, Zoe Paganini, Michela Teh, Tian Huey Rieser, Verena Aroyo, Lora |
| contents | Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends beyond structured safety categories. Many reflect on potential harm to others more than to themselves, grounding their judgments in lived experience, collective risk, and sociocultural awareness. Beyond individual perceptions, we also find that the structure of the task itself -- including annotation guidelines -- shapes how annotators interpret and express harm. Guidelines influence not only which images are flagged, but also the moral judgment behind the justifications. Annotators frequently cite factors such as image quality, visual distortion, and mismatches between prompt and output as contributing to perceived harm dimensions, which are often overlooked in standard evaluation frameworks. Our findings reveal that existing safety pipelines miss critical forms of reasoning that annotators bring to the task. We argue for evaluation designs that scaffold moral reflection, differentiate types of harm, and make space for subjective, context-sensitive interpretations of AI-generated content. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | "Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives Wang, Ding Díaz, Mark Rastogi, Charvi Davani, Aida Prabhakaran, Vinodkumar Mishra, Pushkar Patel, Roma Parrish, Alicia Ashwood, Zoe Paganini, Michela Teh, Tian Huey Rieser, Verena Aroyo, Lora Human-Computer Interaction Artificial Intelligence Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends beyond structured safety categories. Many reflect on potential harm to others more than to themselves, grounding their judgments in lived experience, collective risk, and sociocultural awareness. Beyond individual perceptions, we also find that the structure of the task itself -- including annotation guidelines -- shapes how annotators interpret and express harm. Guidelines influence not only which images are flagged, but also the moral judgment behind the justifications. Annotators frequently cite factors such as image quality, visual distortion, and mismatches between prompt and output as contributing to perceived harm dimensions, which are often overlooked in standard evaluation frameworks. Our findings reveal that existing safety pipelines miss critical forms of reasoning that annotators bring to the task. We argue for evaluation designs that scaffold moral reflection, differentiate types of harm, and make space for subjective, context-sensitive interpretations of AI-generated content. |
| title | "Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2507.16033 |