A Causal Framework for Discovering and Removing Direct and Indirect Discrimination

@article{Zhang2016ACF,
  title={A Causal Framework for Discovering and Removing Direct and Indirect Discrimination},
  author={Lu Zhang and Yongkai Wu and Xintao Wu},
  journal={ArXiv},
  year={2016},
  volume={abs/1611.07509}
}
In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal network to capture the causal structure of the data… 
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112 Citations
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72
Methods Citations
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Results Citations
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112 Citations

Anti-discrimination learning: a causal modeling-based framework

  • Lu ZhangXintao Wu
  • Computer Science
    International Journal of Data Science and Analytics
  • 2017
A causal modeling-based framework for anti-discrimination learning is introduced, two works for discovering and preventing both direct and indirect system-level discrimination in the training data, and a work for extending the non-discrimination result from theTraining data to prediction.

On Discrimination Discovery and Removal in Ranked Data using Causal Graph

This paper studies the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking, and proposes to map the rank position to a continuous score variable that represents the qualification of the candidates.

Achieving Non-Discrimination in Data Release

The key to discrimination discovery and prevention is to find the meaningful partitions that can be used to provide quantitative evidences for the judgment of discrimination, and a simple criterion for the claim of non-discrimination is developed.

Achieving non-discrimination in prediction

This paper adopts the causal model for modeling the data generation mechanism, and formally defining discrimination in population, in a dataset, and in prediction, and develops a two-phase framework for constructing a discrimination-free classifier with a theoretical guarantee.

Marrying Fairness and Explainability in Supervised Learning

This work formalizes direct discrimination as a direct causal effect of the protected attributes on the decisions, while induced discrimination is formalized as a change in the causal influence of non-protected features associated with theprotected attributes.

Identifying Bias in Data Using Two-Distribution Hypothesis Tests

This work identifies biases in training data with respect to proposed distributions and without the need to train a model, and allows it to return a "closest plausible explanation" for a given dataset, potentially revealing underlying biases in the processes that generated them.

A Causal Approach for Unfair Edge Prioritization and Discrimination Removal

It is proved that cumulative unfairness towards sensitive groups in a decision, like race in a bail decision, is non-existent when edge unfairness is absent, and a toolkit to mitigate unfairness during data generation is provided by the Unfair Edge Prioritization algorithm.

Counterfactual Fairness with Partially Known Causal Graph

Interestingly, it is found that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph.

Fair Data Integration

This work proposes an approach to identify a sub-collection of features that ensure the fairness of the dataset by performing conditional independence tests between different subsets of features and theoretically proves the correctness of the proposed algorithm.

Causal Feature Selection for Algorithmic Fairness

This work proposes an approach to identify a sub-collection of features that ensure fairness of the dataset by performing conditional independence tests between different subsets of features, and theoretically proves the correctness of the proposed algorithm and shows that sublinear conditional independent tests are sufficient to identify these variables.
...

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