A Methodology for Direct and Indirect Discrimination Prevention in Data Mining

By Sara Hajian, Josep Domingo-Ferrer. 2013

359 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, 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. Silvia Chiappa
2. Xintao Wu
3. Luca Oneto
4. Lu Zhang
5. Indrė Žliobaitė
6. Aaron Roth
7. Yongkai Wu
8. Saif M. Mohammad

In Safety Research

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

In AI Ethics, Fairness & Society

1. Silvia Chiappa
2. Xintao Wu
3. Lu Zhang
4. Yongkai Wu
5. Luca Oneto
6. Indrė Žliobaitė
7. Sara Hajian
8. Carlos Castillo

Citations in the corpus

• Attributing Fair Decisions with Attention Interventions (2021, background): “This phenomenon is called indirect discrimination (Zliobaite 2015; Hajian and Domingo-Ferrer 2013; Zhang, Wu, and Wu 2017).”
• Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, background): “Discrimination discovery and removal from historical data have received increasing attention over the past few years in data science [1], [2], [3], [4], [5].”
• The Price of Fair PCA: One Extra Dimension (2018, background): “…attribute directly [Pedreshi et al., 2008; Kamiran et al., 2010; Calders and Verwer, 2010; Kamiran and Calders, 2011; Luong et al., 2011; Kamiran et al., 2012; Kamishima et al., 2012; Hajian and Domingo-Ferrer, 2013; Feldman et al., 2015; Zafar et al., 2015; Fish et al., 2016; Adler et al., 2016].”
• Fairness in Machine Learning: Lessons from Political Philosophy (2017, background): “Cognisant of this problem, a burgeoning research paradigm of ‘discrimination-aware data mining’ and ‘fair machine learning’ has emerged, which attempts to detect and mitigate unfairness Hajian and Domingo-Ferrer (2013); Kamiran et al. (2013); Barocas and Selbst (2016).”
• A Convex Framework for Fair Regression (2017, background): “As a result, research on fairness in machine learning and statistics has seen rapid growth in recent years [1, 5–7, 9–11, 13, 14, 18–21, 25, 27], and several mathematical formulations have been proposed as metrics of (un)fairness for a number of different learning frameworks.”
• Achieving Non-Discrimination in Data Release (2016, methodology): “1 INTRODUCTION Discrimination discovery and prevention/removal has been an active research area recently [9, 12, 16, 28, 29, 39].”
• A survey on measuring indirect discrimination in machine learning (2015, methodology): “crimination discovery typically mines association and classifi- cation rules from the data, and then assesses those rules in terms of potential discrimination [Ruggieri et al. 2010; Romei et al. 2012; Hajian and Domingo-Ferrer 2013; Pedreschi et al. 2012; Luong et al. 2011; Mancuhan and Clifton 2014]”
• Fairness in Reinforcement Learning (2016, background): “Early work in data mining [8, 11, 12, 17, 21, 27] considered the question from a primarily empirical standpoint, often using statistical parity as a fairness goal.”
• SemEval-2018 Task 1: Affect in Tweets (2018, background): “Discrimination-aware data mining focuses on measuring discrimination in data (Zliobaite, 2015; Pedreshi et al., 2008; Hajian and Domingo-Ferrer, 2013).”
• Exposing the probabilistic causal structure of discrimination (2015, background): “Some authors [11,17] study how to prevent data mining from becoming itself a source of discrimination.”
• Using Loglinear Model for Discrimination Discovery and Prevention (2016, background): “Keywords-discrimination discovery; discrimination prevention; loglinear modeling; I. INTRODUCTION Discrimination discovery and prevention has been an active research area recently [1]–[4].”
• Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems (2018, methodology): “on measuring discrimination in data as well as on evaluating performance of discrimination-aware predictive models (Zliobaite, 2015; Pedreshi et al., 2008; Hajian and Domingo-Ferrer, 2013; Goh et al., 2016).”
• Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, methodology): “Disparate impact can be stated mathematically in the language of detection theory based on the so-called 80% rule [21], and prevented using newly-developed data science methods [26, 21].”
• Discrimination-aware data mining: a survey (2017, methodology): “The discrimination discovery approach stated by Ruggieri et al. (2010a) is extended by Hajian and Domingo-Ferrer (2013).”