Using Loglinear Model for Discrimination Discovery and Prevention

By Yongkai Wu, Xintao Wu. 2016

16 citations recorded by the source corpus.

Discrimination discovery and prevention has received intensive attention recently. Discrimination generally refers to an unjustified distinction of individuals based on their membership, or perceived membership, in a certain group, and often occurs when the group is treated less favorably than others. However, existing discrimination discovery and prevention approaches are often limited to examining the relationship between one decision attribute and one protected attribute and do not sufficiently incorporate the effects due to other non-protected attributes. In this paper we develop a single unifying framework that aims to capture and measure discriminations between multiple decision attributes and protected attributes in addition to a set of non-protected attributes. Our approach is based on loglinear modeling. The coefficient values of the fitted loglinear model provide quantitative evidence of discrimination in decision making. The conditional independence graph derived from the fitted graphical loglinear model can be effectively used to capture the existence of discrimination patterns based on Markov properties. We further develop an algorithm to remove discrimination. The idea is modifying those significant coefficients from the fitted loglinear model and using the modified model to generate new data. Our empirical evaluation results show effectiveness of our proposed approach.

Subfields: Safety Research, Sociology and Political Science, 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 Safety Research

1. Lu Zhang
2. Silvia Chiappa
3. Xintao Wu
4. Luca Oneto
5. Yongkai Wu
6. Dana Pessach
7. Erez Shmueli
8. Fred Morstatter

In Sociology and Political Science

1. Silvia Chiappa
2. Lu Zhang
3. Luca Oneto
4. Yongkai Wu
5. Xintao Wu
6. Christian Haas
7. Simon Caton

In AI Ethics, Fairness & Society

1. Silvia Chiappa
2. Lu Zhang
3. Xintao Wu
4. Yongkai Wu
5. Luca Oneto
6. Depeng Xu
7. Shuhan Yuan
8. Fred Morstatter

Citations in the corpus

• Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, background): “Data preprocessingmethods [2], [5], [6], [17]modify the historical data to remove discriminatory patterns.”
• FairGAN: Fairness-aware Generative Adversarial Networks (2018, background): “remove discriminatory effect before conducting predictive analytics [4], [7], [11]–[13], the in-process approaches that enforce fairness to classifiers by introducing constraints or regularization terms to the objective functions [14], [15], and the post-process approaches that directly change the p”
• Achieving Non-Discrimination in Data Release (2016, methodology): “Proposed methods for discrimination prevention using algorithm tweaking require some tweak of predictive models, including the decision tree [17], the naive bayes classifier [5], the logistic regression [18], and the log-linear model [33].”
• Fairness in Machine Learning (2020, methodology): “…21, 22, 23, 25, 28, 29, 31, 34, 44, 45, 46, 52, 53, 54, 55, 56, 59, 66, 69, 71, 72, 73, 74, 75, 82, 84, 87, 89, 90, 94, 95, 97, 98, 99, 103, 104, 106, 114, 115, 116, 121, 123, 126, 138, 139, 141, 148, 149, 153, 156, 159, 165, 174, 176, 177, 178, 179, 180, 182, 183, 184, 186, 187, 191, 192, 193].”
• On Discrimination Discovery Using Causal Networks (2016, methodology): “In [22], the authors proposed the use of loglinear modeling to capture and measure discrimination and developed a method for discrimination prevention by modifying significant coefficients from the fitted loglinear model.”