Data-Driven Discrimination at Work

By P. Kim. 2017

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Citations in the corpus

What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (2019, background): “When the performance of a machine learning system differs substantially for different groups of people, a number of concerns arise (Barocas and Selbst, 2016; Kim, 2016).”
Explaining Explanations in AI (2018, background): “ely attributing a decision to an irrelevant feature, or a more acceptable feature (e.g. post code, ’leadership’) that serves as a proxy for a legally protected feature (cf. Barocas and Selbst (2016); Kim (2016)). These risks may be mitigated by developing methods to provide contrastive, selective, a”
Mitigating bias in algorithmic hiring: evaluating claims and practices (2019, background): “uld be noted that this description is based on a particular (although the most common) interpretation of Title VII. Some legal scholars contend that Title VII oers stronger protections to minorities [15, 54], and there is disagreement on how (or whether) to operationalize the 4/5 rule through statis”
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting (2019, background): “butes may disadvantage groups for whom the chosen attributes are not equally predictive of the target label [12]. Barocas and Selbst [4] discussed these issues in the context of automated hiring; Kim [28] explained how data-driven decisions that systematically bias people’s access to opportunities r”
Preventing undesirable behavior of intelligent machines (2019, background): “Kim [112] discusses the subtleties that arise when applying the existing legal doctrines of disparate treatment and disparate impact to decisions made by machine learning agents.”
On the Legal Compatibility of Fairness Definitions (2019, background): “Others in the legal community, however, [29; 33] have interpreted Ricci more narrowly, concluding that it would not limit most efforts to correct for bias since the Court in this case was heavily motivated by the fact that many firefighters spent significant time and money preparing for the test, an”
Discrimination in Online Advertising: A Multidisciplinary Inquiry (2018, background): “Kim (2016) explores the application of antidiscrimination norms of Title VII to computers making employment decisions and argues that this requires reassessment of the laws.”
Does mitigating ML's impact disparity require treatment disparity? (2017, background): “While disparate treatment is by definition illegal, the status of treatment disparity is debated [9].”