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James Atwood

James Atwood

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    Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
    Tina Tian
    Ben Packer
    Meghana Deodhar
    Alex Beutel
    The Second Workshop on Spurious Correlations, Invariance and Stability @ ICML 2023 (2023)
    Preview abstract Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of multiple classifiers and where multiple groups are present. In this paper, we first show that natural baseline approaches for improving equal opportunity fairness scale linearly with the product of the number of remediated groups and the number of remediated prediction labels, rendering them impractical. We then introduce two simple techniques, called task-overconditioning and group-interleaving, to achieve a constant scaling in this multi-group multi-label setup. Our experimental results in academic and real-world environments demonstrate the effectiveness of our proposal at mitigation within this environment. View details
    The Inclusive Images Competition
    Igor Ivanov
    Miha Skalic
    Pallavi Baljekar
    Pavel Ostyakov
    Roman Solovyev
    Weimin Wang
    Yoni Halpern
    Springer Series (2019)
    Preview abstract Popular large image classification datasets that are drawn from the web present Eurocentric and Americentric biases that negatively impact the generalizability of models trained on them . In order to encourage the development of modeling approaches that generalize well to images drawn from locations and cultural contexts that are unseen or poorly represented at the time of training, we organized the Inclusive Images competition in association with Kaggle and the NeurIPS 2018 Competition Track Workshop. In this chapter, we describe the motivation and design of the competition, present reports from the top three competitors, and provide high-level takeaways from the competition results. View details
    BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
    Yoni Halpern
    Neural Information Processing Systems: Workshop on Ethical, Social and Governance Issues in AI (2018)
    Preview abstract We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demographic parity with respect to the sensitive information. The net result is that these BriarPatches provide an intervention mechanism available at user level, and complements prior research on fair representations that were previously only applicable by model developers and ML experts. View details
    Preview abstract Modern machine learning systems such as image classifers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. Such strategies may be particularly important for ML applications in the developing world, where resources may be constrained and the cost of creating suitable large scale data sets may be a blocking factor. However, we suggest that examining the {\em geo-diversity} of open data sets is critical before adopting a data set for such use cases. In particular, we analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit a observable amerocentric and eurocentric representation bias. Further, we perform targeted analysis on classifiers that use these data sets as training data to assess the impact of these training distributions, and find strong differences in the relative performance on images from different locales. These results emphasize the need to ensure geo-representation when constructing data sets for use in the developing world. View details
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