Professor Dr. Bettina Berendt of KU Leuven and Dr. Sören Preibusch of Microsoft Research have published Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence, forthcoming in Artificial Intelligence and Law.
Here is the abstract:
Decision makers in banking, insurance or employment mitigate many of their risks by telling “good” individuals and “bad” individuals apart. Laws codify societal understandings of which factors are legitimate grounds for differential treatment (and when and in which contexts)—or are considered unfair discrimination, including gender, ethnicity or age. Discrimination-aware data mining (DADM) implements the hope that information technology supporting the decision process can also keep it free from unjust grounds. However, constraining data mining to exclude a fixed enumeration of potentially discriminatory features is insufficient. We argue for complementing it with exploratory DADM, where discriminatory patterns are discovered and flagged rather than suppressed. This article discusses the relative merits of constraint-oriented and exploratory DADM from a conceptual viewpoint. In addition, we consider the case of loan applications to empirically assess the fitness of both discrimination-aware data mining approaches for two of their typical usage scenarios: prevention and detection. Using Mechanical Turk, 215 US-based participants were randomly placed in the roles of a bank clerk (discrimination prevention) or a citizen / policy advisor (detection). They were tasked to recommend or predict the approval or denial of a loan, across three experimental conditions: discrimination-unaware data mining, exploratory, and constraint-oriented DADM (eDADM resp. cDADM). The discrimination-aware tool support in the eDADM and cDADM treatments led to significantly higher proportions of correct decisions, which were also motivated more accurately. There is significant evidence that the relative advantage of discrimination-aware techniques depends on their intended usage. For users focussed on making and motivating their decisions in non-discriminatory ways, cDADM resulted in more accurate and less discriminatory results than eDADM. For users focussed on monitoring for preventing discriminatory decisions and motivating these conclusions, eDADM yielded more accurate results than cDADM.’
Filed under: Applications, Articles and papers, Research findings, Technology developments, Technology tools Tagged: Antidiscrimination law enforcement systems, Antidiscrimination law information systems, Antidiscrimination law prevention systems, Artificial intelligence and law, Bettina Berendt, Business lending information systems, cDADM, Constraint-oriented discrimination-aware data mining, Consumer credit information systems, Consumer law information systems, Consumer lending information systems, Contraint-oriented DADM, DADM, Data mining and antidiscrimination law, Data mining and legal compliance, Discrimination-aware data mining, eDADM, Experimental methods in legal informatics, Exploratory DADM, Exploratory discrimination-aware data mining, Financial law information systems, Legal compliance information systems, Legal compliance systems, Legal data mining, Legal data mining for discrimination, Modeling antidiscrimination laws, Modeling antidiscrimination rules, Modeling legal rules, Sören Preibusch![]()
