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Evaluating the Fairness of Discriminative Foundation Models in Computer Vision

Published: 29 August 2023 Publication History

Abstract

We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI’s CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning. We categorize desired behaviors based around three axes: (i) if the task concerns humans; (ii) how subjective the task is (i.e., how likely it is that people from a diverse range of backgrounds would agree on a labeling); and (iii) the intended purpose of the task and if fairness is better served by impartiality (i.e., making decisions independent of the protected attributes) or representation (i.e., making decisions to maximize diversity). Finally, we provide quantitative fairness evaluations for both binary-valued and multi-valued protected attributes over ten diverse datasets. We find that fair PCA, a post-processing method for fair representations, works very well for debiasing in most of the aforementioned tasks while incurring only minor loss of performance. However, different debiasing approaches vary in their effectiveness depending on the task. Hence, one should choose the debiasing approach depending on the specific use case.

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Cited By

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  • (2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692594(13072-13085)Online publication date: 21-Jul-2024
  • (2024)Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694702(1-17)Online publication date: 29-Oct-2024
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  1. Evaluating the Fairness of Discriminative Foundation Models in Computer Vision

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      cover image ACM Conferences
      AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society
      August 2023
      1026 pages
      ISBN:9798400702310
      DOI:10.1145/3600211
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Published: 29 August 2023

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      1. AI fairness
      2. evaluation
      3. foundation models

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      AIES '23: AAAI/ACM Conference on AI, Ethics, and Society
      August 8 - 10, 2023
      QC, Montr\'{e}al, Canada

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      Overall Acceptance Rate 61 of 162 submissions, 38%

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      View all
      • (2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692594(13072-13085)Online publication date: 21-Jul-2024
      • (2024)Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694702(1-17)Online publication date: 29-Oct-2024
      • (2024)Pattern Recognition and Prediction in Time Series Data Through Retrieval-Augmented Techniques2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)10.1109/ICEECT61758.2024.10738936(1-6)Online publication date: 29-Aug-2024
      • (2024)Parrot Captions Teach CLIP to Spot TextComputer Vision – ECCV 202410.1007/978-3-031-72946-1_21(368-385)Online publication date: 2-Oct-2024

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