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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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
It is argued that the concept of fairness requires causal reasoning, and existing works and future opportunities for applying data management techniques to causal algorithmic fairness are identified.
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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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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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OmniFair is shown to be more versatile than existing algorithmic fairness approaches in terms of both supported fairness constraints and downstream ML models, and to be up to 270x faster than in-processing methods.
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