Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and efficiency, several potential problems have yet to be addressed, particularly the potential for unintentional discrimination. We present an iterative procedure, based on orthogonal projection of input attributes, for enabling interpretability of black-box predictive models. Through our iterative procedure, one can quantify the relative dependence of a black-box model on its input attributes.The relative significance of the inputs to a predictive model can then be used to assess the fairness (or discriminatory extent) of such a model.
As another class of methodologies, algorithm manipulation methods seek to augment the underlying algorithm in order to reduce discrimination. Algorithm augmentation is usually done via a penalty that adds a cost of discrimination to a model’s cost function. These algorithms typically add regularizers that quantify the degree of bias. A seminal work in this area is the study by Kamishima et. al. in @cite_4 where they quantify prejudice by adding a mutual information based regularizer to the cost function of a logistic regression model. Since the Kamishima et. al. work, more approaches that seek to change underlying cost functions with regularizers for statistical parity have emerged for other kinds of algorithms like decision trees and support vector machines. Techniques presented in this area typically only work for one particular method like logistic regression or Naive Bayes, so the overall impact can be limited. Algorithm manipulation methods also assume that underlying predictive models are known, completely specified with well-behaved cost functions. Usually, this is not the case, as a variety of models are typically combined in a number of ways where it becomes difficult to untangle the cost function for the combined model.
@cite_4
2040825624
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people's lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be socially and legally fair from a viewpoint of social responsibility, namely, it must be unbiased and nondiscriminatory in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.