Proposes a three-level taxonomy of Cultural Awareness, Cultural Sensitivity, and Cultural Competence for AI evaluation, grounded in intercultural communication scholarship to improve validity in multicultural contexts.
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , pages=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
DR-Smoothing introduces a disrupt-then-rectify prompt processing scheme into smoothing defenses, delivering tight theoretical bounds on success probability against both token- and prompt-level jailbreaks.
Social identity markers in medical questions degrade LLM accuracy and uncertainty calibration, producing a calibration crisis that is non-additive for intersectional cases.
LLMs exhibit reproducible asymmetries in advice on hypothetical religious conversions, favoring Catholic, Bahá'í, and Sikh transitions while disfavoring Atheist, Agnostic, and Jehovah's Witness ones across 20 models and 182 pairings.
citing papers explorer
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Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory
Proposes a three-level taxonomy of Cultural Awareness, Cultural Sensitivity, and Cultural Competence for AI evaluation, grounded in intercultural communication scholarship to improve validity in multicultural contexts.
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Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
DR-Smoothing introduces a disrupt-then-rectify prompt processing scheme into smoothing defenses, delivering tight theoretical bounds on success probability against both token- and prompt-level jailbreaks.
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Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Social identity markers in medical questions degrade LLM accuracy and uncertainty calibration, producing a calibration crisis that is non-additive for intersectional cases.
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When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance
LLMs exhibit reproducible asymmetries in advice on hypothetical religious conversions, favoring Catholic, Bahá'í, and Sikh transitions while disfavoring Atheist, Agnostic, and Jehovah's Witness ones across 20 models and 182 pairings.