Bias Mitigation Post-processing for Individual and Group Fairness

By P. Lohia, Karthikeyan Natesan Ramamurthy, M. Bhide, Diptikalyan Saha, Kush R. Varshney, R. Puri. 2018

193 citations recorded by the source corpus.

Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation algorithm aiming to improve the group fairness measure of disparate impact. We show superior performance to previous work in the combination of classification accuracy, individual fairness and group fairness on several real-world datasets in applications such as credit, employment, and criminal justice.

Subfields: Artificial Intelligence, Safety Research

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. Dana Pessach
2. Rogério Schmidt Feris
3. Erez Shmueli
4. John R. Smith
5. Michele Merler
6. Sinead Williamson
7. Nalini K. Ratha
8. Guy W. Cole

In Safety Research

1. Dana Pessach
2. Erez Shmueli
3. Sinead Williamson
4. Nalini K. Ratha
5. Guy W. Cole

Citations in the corpus

• Diversity in Faces (2019, background): “The study of bias and fairness has recently gained broad interest in computer vision and machine learning [43–45].”
• A Review on Fairness in Machine Learning (2022, background): “[138] propose a combined approach to both individual and group fairness; and Lahoti et al.”
• Avoiding Resentment Via Monotonic Fairness (2019, methodology): “[32], have attempted to use post hoc adjustments and model pooling prevent biases in machine learning.”