Certifying Robustness to Programmable Data Bias in Decision TreesDownload PDF

21 May 2021, 20:51 (modified: 22 Jan 2022, 17:02)NeurIPS 2021 PosterReaders: Everyone
Keywords: data bias, data poisoning, robustness, decision trees, fairness
Abstract: Datasets can be biased due to societal inequities, human biases, under-representation of minorities, etc. Our goal is to certify that models produced by a learning algorithm are pointwise-robust to dataset biases. This is a challenging problem: it entails learning models for a large, or even infinite, number of datasets, ensuring that they all produce the same prediction. We focus on decision-tree learning due to the interpretable nature of the models. Our approach allows programmatically specifying \emph{bias models} across a variety of dimensions (e.g., label-flipping or missing data), composing types of bias, and targeting bias towards a specific group. To certify robustness, we use a novel symbolic technique to evaluate a decision-tree learner on a large, or infinite, number of datasets, certifying that each and every dataset produces the same prediction for a specific test point. We evaluate our approach on datasets that are commonly used in the fairness literature, and demonstrate our approach's viability on a range of bias models.
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Code: https://github.com/annapmeyer/antidote-P
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