4,030 research outputs found
Certifying and removing disparate impact
What does it mean for an algorithm to be biased? In U.S. law, unintentional
bias is encoded via disparate impact, which occurs when a selection process has
widely different outcomes for different groups, even as it appears to be
neutral. This legal determination hinges on a definition of a protected class
(ethnicity, gender, religious practice) and an explicit description of the
process.
When the process is implemented using computers, determining disparate impact
(and hence bias) is harder. It might not be possible to disclose the process.
In addition, even if the process is open, it might be hard to elucidate in a
legal setting how the algorithm makes its decisions. Instead of requiring
access to the algorithm, we propose making inferences based on the data the
algorithm uses.
We make four contributions to this problem. First, we link the legal notion
of disparate impact to a measure of classification accuracy that while known,
has received relatively little attention. Second, we propose a test for
disparate impact based on analyzing the information leakage of the protected
class from the other data attributes. Third, we describe methods by which data
might be made unbiased. Finally, we present empirical evidence supporting the
effectiveness of our test for disparate impact and our approach for both
masking bias and preserving relevant information in the data. Interestingly,
our approach resembles some actual selection practices that have recently
received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on
Knowledge Discovery and Data Minin
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the errors (or, misclassifications) over the given historical data.
However, it is quite possible that the optimally trained classifier makes
decisions for people belonging to different social groups with different
misclassification rates (e.g., misclassification rates for females are higher
than for males), thereby placing these groups at an unfair disadvantage. To
account for and avoid such unfairness, in this paper, we introduce a new notion
of unfairness, disparate mistreatment, which is defined in terms of
misclassification rates. We then propose intuitive measures of disparate
mistreatment for decision boundary-based classifiers, which can be easily
incorporated into their formulation as convex-concave constraints. Experiments
on synthetic as well as real world datasets show that our methodology is
effective at avoiding disparate mistreatment, often at a small cost in terms of
accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web
Conference (WWW), 2017. Code available at:
https://github.com/mbilalzafar/fair-classificatio
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
As virtually all aspects of our lives are increasingly impacted by
algorithmic decision making systems, it is incumbent upon us as a society to
ensure such systems do not become instruments of unfair discrimination on the
basis of gender, race, ethnicity, religion, etc. We consider the problem of
determining whether the decisions made by such systems are discriminatory,
through the lens of causal models. We introduce two definitions of group
fairness grounded in causality: fair on average causal effect (FACE), and fair
on average causal effect on the treated (FACT). We use the Rubin-Neyman
potential outcomes framework for the analysis of cause-effect relationships to
robustly estimate FACE and FACT. We demonstrate the effectiveness of our
proposed approach on synthetic data. Our analyses of two real-world data sets,
the Adult income data set from the UCI repository (with gender as the protected
attribute), and the NYC Stop and Frisk data set (with race as the protected
attribute), show that the evidence of discrimination obtained by FACE and FACT,
or lack thereof, is often in agreement with the findings from other studies. We
further show that FACT, being somewhat more nuanced compared to FACE, can yield
findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the
International Conference on World Wide Web (WWW), 201
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