Nuanced Safety for Generative AI: How Demographics Shape Responsiveness to Severity

• URL: http://arxiv.org/abs/2503.05609v1
• Date: Fri, 07 Mar 2025 17:32:31 GMT
• Title: Nuanced Safety for Generative AI: How Demographics Shape Responsiveness to Severity
• Authors: Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl, Alicia Parrish, Roma Patel, Mark Diaz, Ding Wang, Michela Paganini, Vinodkumar Prabhakaran, Lora Aroyo, Verena Rieser,
• Abstract summary: We introduce a novel data-driven approach for calibrating granular ratings in pluralistic datasets.<n>We distill non-parametric responsiveness metrics that quantify the consistency of raters in scoring the varying levels of the severity of safety violations.<n>We show that our approach offers improved capabilities for prioritizing safety concerns by capturing nuanced viewpoints across different demographic groups.
• Score: 28.05638097604126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for calibrating granular ratings in pluralistic datasets. Specifically, we address the challenge of interpreting responses of a diverse population to safety expressed via ordinal scales (e.g., Likert scale). We distill non-parametric responsiveness metrics that quantify the consistency of raters in scoring the varying levels of the severity of safety violations. Using safety evaluation of AI-generated content as a case study, we investigate how raters from different demographic groups (age, gender, ethnicity) use an ordinal scale to express their perception of the severity of violations in a pluralistic safety dataset. We apply our metrics across violation types, demonstrating their utility in extracting nuanced insights that are crucial for developing reliable AI systems in a multi-cultural contexts. We show that our approach offers improved capabilities for prioritizing safety concerns by capturing nuanced viewpoints across different demographic groups, hence improving the reliability of pluralistic data collection and in turn contributing to more robust AI evaluations.

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