FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce discrimination in their predictions. This paper proposes a method to post-process the predictions of a predictive model to make the processed predictions non-discriminatory. The method considers multiple protected variables together. Multiple protected variables make the problem more challenging than a simple protected variable. The method uses a well-cited discrimination metric and adapts it to allow the specification of explanatory variables, such as position, profession, education, that describe the contexts of the applications. It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time. The proposed method is independent of classification methods. It can handle the cases that existing methods cannot handle: satisfying multiple protected attributes at the same time, allowing multiple explanatory attributes, and being independent of classification model types. An evaluation using four real world data sets shows that the proposed method is as effectively as existing methods, in addition to its extra power.

[1]  Seth Neel,et al.  Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness , 2017, ICML.

[2]  Krishna P. Gummadi,et al.  Fairness Constraints: A Mechanism for Fair Classification , 2015, ArXiv.

[3]  Xiangliang Zhang,et al.  Anti-discrimination Analysis Using Privacy Attack Strategies , 2014, ECML/PKDD.

[4]  Toon Calders,et al.  Handling Conditional Discrimination , 2011, 2011 IEEE 11th International Conference on Data Mining.

[5]  Lu Zhang,et al.  Achieving Non-Discrimination in Data Release , 2016, KDD.

[6]  Toon Calders,et al.  Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.

[7]  Toniann Pitassi,et al.  Learning Fair Representations , 2013, ICML.

[8]  Benjamin Fish,et al.  A Confidence-Based Approach for Balancing Fairness and Accuracy , 2016, SDM.

[9]  Nathan Srebro,et al.  Equality of Opportunity in Supervised Learning , 2016, NIPS.

[10]  Carlos Eduardo Scheidegger,et al.  Certifying and Removing Disparate Impact , 2014, KDD.

[11]  Krishna P. Gummadi,et al.  Fairness Constraints: Mechanisms for Fair Classification , 2015, AISTATS.

[12]  James Bailey,et al.  Discrimination aware classification for imbalanced datasets , 2013, CIKM.

[13]  Jon M. Kleinberg,et al.  Inherent Trade-Offs in the Fair Determination of Risk Scores , 2016, ITCS.

[14]  Franco Turini,et al.  Measuring Discrimination in Socially-Sensitive Decision Records , 2009, SDM.

[15]  Jun Sakuma,et al.  Model-based and actual independence for fairness-aware classification , 2017, Data Mining and Knowledge Discovery.

[16]  Steven Mills,et al.  Fair Forests: Regularized Tree Induction to Minimize Model Bias , 2017, AIES.

[17]  Lu Zhang,et al.  On Discrimination Discovery Using Causal Networks , 2016, SBP-BRiMS.

[18]  Toniann Pitassi,et al.  Fairness through awareness , 2011, ITCS '12.

[19]  Suresh Venkatasubramanian,et al.  A comparative study of fairness-enhancing interventions in machine learning , 2018, FAT.

[20]  Steven Ruggles,et al.  Integrated Public Use Microdata Series: Version 3 , 2003 .

[21]  Jun Sakuma,et al.  Prediction with Model-Based Neutrality , 2013, ECML/PKDD.

[22]  Francesco Bonchi,et al.  Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining , 2016, KDD.

[23]  Franco Turini,et al.  k-NN as an implementation of situation testing for discrimination discovery and prevention , 2011, KDD.

[24]  Toon Calders,et al.  Discrimination Aware Decision Tree Learning , 2010, 2010 IEEE International Conference on Data Mining.

[25]  Indră Liobaită.  Measuring discrimination in algorithmic decision making , 2017 .

[26]  Virgile Landeiro,et al.  Robust Text Classification in the Presence of Confounding Bias , 2016, AAAI.

[27]  Jun Sakuma,et al.  Fairness-Aware Classifier with Prejudice Remover Regularizer , 2012, ECML/PKDD.

[28]  Jiuyong Li,et al.  Discrimination detection by causal effect estimation , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[29]  Chris Clifton,et al.  Combating discrimination using Bayesian networks , 2014, Artificial Intelligence and Law.

[30]  Nathan Srebro,et al.  Learning Non-Discriminatory Predictors , 2017, COLT.

[31]  Lu Zhang,et al.  Situation Testing-Based Discrimination Discovery: A Causal Inference Approach , 2016, IJCAI.