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Title: Fairness Testing: Testing Software for Discrimination
This paper defines the notions of software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates. Specifically, the paper focuses on measuring causality in discriminatory behavior. Modern software contributes to important societal decisions and evidence of software discrimination has been found in systems that recommend criminal sentences, grant access to financial loans and products, and determine who is allowed to participate in promotions and receive services. Our approach, Themis, measures discrimination in software by generating efficient, discrimination-testing test suites. Given a schema describing valid system inputs, Themis generates discrimination tests automatically and, notably, does not require an oracle. We evaluate Themis on 20 software systems, 12 of which come from prior work with explicit focus on avoiding discrimination. We find that (1) Themis is effective at discovering software discrimination, (2) state-of-the-art techniques for removing discrimination from algorithms fail in many situations, at times discriminating against as much as 98% of an input subdomain, (3) Themis optimizations are effective at producing efficient test suites for measuring discrimination, and (4) Themis is more efficient on systems that exhibit more discrimination. We thus demonstrate that fairness testing is a critical aspect of the software development cycle more » in domains with possible discrimination and provide initial tools for measuring software discrimination. « less
Authors:
; ;
Award ID(s):
1453543 1744471
Publication Date:
NSF-PAR ID:
10033437
Journal Name:
Proceedings of 2017 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering
Page Range or eLocation-ID:
498–510
Sponsoring Org:
National Science Foundation
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