Interventional Fairness: Causal Database Repair for Algorithmic Fairness

@article{Salimi2019InterventionalFC,
  title={Interventional Fairness: Causal Database Repair for Algorithmic Fairness},
  author={Babak Salimi and Luke Rodriguez and Bill Howe and Dan Suciu},
  journal={Proceedings of the 2019 International Conference on Management of Data},
  year={2019}
}
Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification… 
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94 Citations
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94 Citations

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Citation Type

Capuchin: Causal Database Repair for Algorithmic Fairness
TLDR
This paper formalizes the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model and using these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels.
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This paper formalizes the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model and using these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels.
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TLDR
This paper is a compilation of the major identifiability results which are of particular relevance for machine learning fairness and is of particular interest to fairness researchers, practitioners, and pol-icy makers who are considering the use of causality-based fairness notions.
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A novel multi-objective feature selection strategy that leverages feature construction to generate more features that lead to both high accuracy and fairness on three well-known datasets achieves higher accuracy than other fairness-aware approaches while maintaining similar or higher fairness.
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This work considers a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge, and proposes an approach that performs conditional independence tests on observed data to identify proxy attributes that are causally dependent on the (unknown) protected attribute.
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TLDR
Gopher is introduced, a system that produces compact, interpretable, and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior, and the concept of causal responsibility is introduced that quantifies the extent to which intervening on training data by removing or updating subset of it can resolve the bias.
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