You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings

Zeerak Talat · Aurélie Névéol A · Stella Biderman · Miruna Clinciu · Manan Dey · Shayne Longpre +10 more

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Ethics and Social Impacts of AI Natural Language Processing Techniques Topic Modeling

Abstract

Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla, Oskar Van Der Wal. Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models. 2022.

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1991Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color 1988Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective 2023Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics 1990Rethinking the Public Sphere: A Contribution to the Critique of Actually Existing Democracy 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2014Big Data's Disparate Impact 2017Semantics derived automatically from language corpora contain human-like biases 2019Model Cards for Model Reporting

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2023Fairness in Language Models Beyond English: Gaps and Challenges 2024Future Shock: Generative AI and the International AI Policy and Governance Crisis 2024Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models 2024A Comparative Analysis to Evaluate Bias and Fairness Across Large Language Models with Benchmarks 2023A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models

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