3 research outputs found
Tracking and Improving Information in the Service of Fairness
As algorithmic prediction systems have become widespread, fears that these
systems may inadvertently discriminate against members of underrepresented
populations have grown. With the goal of understanding fundamental principles
that underpin the growing number of approaches to mitigating algorithmic
discrimination, we investigate the role of information in fair prediction. A
common strategy for decision-making uses a predictor to assign individuals a
risk score; then, individuals are selected or rejected on the basis of this
score. In this work, we study a formal framework for measuring the information
content of predictors. Central to this framework is the notion of a refinement,
first studied by Degroot and Fienberg. Intuitively, a refinement of a predictor
increases the overall informativeness of the predictions without losing the
information already contained in . We show that increasing information
content through refinements improves the downstream selection rules across a
wide range of fairness measures (e.g. true positive rates, false positive
rates, selection rates). In turn, refinements provide a simple but effective
tool for reducing disparity in treatment and impact without sacrificing the
utility of the predictions. Our results suggest that in many applications, the
perceived "cost of fairness" results from an information disparity across
populations, and thus, may be avoided with improved information.Comment: Appeared at EC 201
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
A trade-off between accuracy and fairness is almost taken as a given in the
existing literature on fairness in machine learning. Yet, it is not preordained
that accuracy should decrease with increased fairness. Novel to this work, we
examine fair classification through the lens of mismatched hypothesis testing:
trying to find a classifier that distinguishes between two ideal distributions
when given two mismatched distributions that are biased. Using Chernoff
information, a tool in information theory, we theoretically demonstrate that,
contrary to popular belief, there always exist ideal distributions such that
optimal fairness and accuracy (with respect to the ideal distributions) are
achieved simultaneously: there is no trade-off. Moreover, the same classifier
yields the lack of a trade-off with respect to ideal distributions while
yielding a trade-off when accuracy is measured with respect to the given
(possibly biased) dataset. To complement our main result, we formulate an
optimization to find ideal distributions and derive fundamental limits to
explain why a trade-off exists on the given biased dataset. We also derive
conditions under which active data collection can alleviate the
fairness-accuracy trade-off in the real world. Our results lead us to contend
that it is problematic to measure accuracy with respect to data that reflects
bias, and instead, we should be considering accuracy with respect to ideal,
unbiased data.Comment: This paper appears in the Proceedings of the 37th International
Conference on Machine Learning, pp. 2803--2813, 202
Fairness Under Feature Exemptions: Counterfactual and Observational Measures
With the growing use of AI in highly consequential domains, the
quantification and removal of bias with respect to protected attributes, such
as gender, race, etc., is becoming increasingly important. While quantifying
bias is essential, sometimes the needs of a business (e.g., hiring) may require
the use of certain features that are critical in a way that any bias that can
be explained by them might need to be exempted. E.g., a standardized test-score
may be a critical feature that should be weighed strongly in hiring even if
biased, whereas other features, such as zip code may be used only to the extent
that they do not discriminate. In this work, we propose a novel
information-theoretic decomposition of the total bias (in a counterfactual
sense) into a non-exempt component that quantifies the part of the bias that
cannot be accounted for by the critical features, and an exempt component which
quantifies the remaining bias. This decomposition allows one to check if the
bias arose purely due to the critical features (inspired from the business
necessity defense of disparate impact law) and also enables selective removal
of the non-exempt component if desired. We arrive at this decomposition through
examples that lead to a set of desirable properties (axioms) that any measure
of non-exempt bias should satisfy. We demonstrate that our proposed
counterfactual measure satisfies all of them. Our quantification bridges ideas
of causality, Simpson's paradox, and a body of work from information theory
called Partial Information Decomposition. We also obtain an impossibility
result showing that no observational measure of non-exempt bias can satisfy all
of the desirable properties, which leads us to relax our goals and examine
observational measures that satisfy only some of these properties. We then
perform case studies to show how one can train models while reducing non-exempt
bias.Comment: Journal version (Shorter version was accepted at AAAI 2020 as an oral
presentation