On Discrimination Discovery Using Causal Networks

By Lu Zhang, Yongkai Wu, Xintao Wu. 2016

22 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, Causal Inference & Discovery, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xintao Wu
2. Lu Zhang
3. Fred Morstatter
4. Ninareh Mehrabi
5. Aram Galstyan
6. Yongkai Wu
7. Nripsuta Ani Saxena
8. Kristina Lerman

In Safety Research

1. Lu Zhang
2. Xintao Wu
3. Yongkai Wu
4. Aram Galstyan
5. Fred Morstatter
6. Ninareh Mehrabi
7. Nripsuta Ani Saxena
8. Kristina Lerman

In Causal Inference & Discovery

1. Xintao Wu
2. Lu Zhang
3. Yongkai Wu
4. Aram Galstyan
5. Fred Morstatter
6. Ninareh Mehrabi
7. Kristina Lerman
8. Nripsuta Ani Saxena

In AI Ethics, Fairness & Society

1. Lu Zhang
2. Xintao Wu
3. Yongkai Wu
4. Fred Morstatter
5. Ninareh Mehrabi
6. Aram Galstyan
7. Kristina Lerman
8. Nripsuta Ani Saxena

Citations in the corpus

• A Survey on Bias and Fairness in Machine Learning (2019, methodology): “usal discrimination) or to covariates that have not been accounted for in the analysis (omitted variable bias). This RDc then becomes their causal discrimination measure for discrimination discovery. [133] is another work of this type that uses causal networks for discrimination discovery. 5.3 Fair ”
• Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, methodology): “Studies in [7], [8], [9] are built on causal modeling and the associated causal graph, but cannot deal with indirect discrimination.”