Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness

Caner Hazirbas; Yejin Bang; Tiezheng Yu; Parisa Assar; Bilal Porgali; Vítor Albiero; Stefan Hermanek; Jacqueline Pan; Emily McReynolds; Miranda Bogen; Pascale Fung; Cristian Canton Ferrer

arXiv · 2211.05809

Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness

Caner Hazirbas·Yejin Bang·Tiezheng Yu·Parisa Assar·Bilal Porgali·Vítor Albiero·Stefan Hermanek·Jacqueline Pan·Emily McReynolds·Miranda Bogen·Pascale Fung·Cristian Canton Ferrer

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Abstract

Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, therefore, focus on collecting person-related datasets that have carefully selected labels, including sensitive characteristics, and consent forms in place to use those attributes for model testing and development. Responsible data collection involves several stages, including but not limited to determining use-case scenarios, selecting categories (annotations) such that the data are fit for the purpose of measuring algorithmic bias for subgroups and most importantly ensure that the selected categories/subcategories are robust to regional diversities and inclusive of as many subgroups as possible. Meta, in a continuation of our efforts to measure AI algorithmic bias and robustness (https://ai.facebook.com/blog/shedding-light-on-fairness-in-ai-with-a-new-data-set), is working on collecting a large consent-driven dataset with a comprehensive list of categories. This paper describes our proposed design of such categories and subcategories for Casual Conversations v2.

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Caner Hazirbas, Yejin Bang, Tiezheng Yu, Parisa Assar, Bilal Porgali, Vítor Albiero, Stefan Hermanek, Jacqueline Pan, Emily McReynolds, Miranda Bogen, Pascale Fung, Cristian Canton Ferrer. 2022-11-10. Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness. https://arxiv.org/abs/2211.05809

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