Rethinking AI Cultural Evaluation

• URL: http://arxiv.org/abs/2501.07751v1
• Date: Mon, 13 Jan 2025 23:42:37 GMT
• Title: Rethinking AI Cultural Evaluation
• Authors: Michal Bravansky, Filip Trhlik, Fazl Barez,
• Abstract summary: Current evaluation methods predominantly rely on multiple-choice question (MCQ) datasets. Our findings highlight significant discrepancies between MCQ-based assessments and the values conveyed in unconstrained interactions. We recommend moving beyond MCQs to adopt more open-ended, context-specific assessments.
• Score: 1.8434042562191815
• License:
• Abstract: As AI systems become more integrated into society, evaluating their capacity to align with diverse cultural values is crucial for their responsible deployment. Current evaluation methods predominantly rely on multiple-choice question (MCQ) datasets. In this study, we demonstrate that MCQs are insufficient for capturing the complexity of cultural values expressed in open-ended scenarios. Our findings highlight significant discrepancies between MCQ-based assessments and the values conveyed in unconstrained interactions. Based on these findings, we recommend moving beyond MCQs to adopt more open-ended, context-specific assessments that better reflect how AI models engage with cultural values in realistic settings.

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