economicsalgorithmsbiasfairnessWe develop an economic perspective on algorithmic fairness and the surrounding empirical, theoretical and policy issues. Our perspective draws from clear paralldoi:10.2139/ssrn.3361280Cowgill, BoTucker, Catherine E.Social Science Electronic Publishing...
In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS...
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The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized ...
and strategic considerations: e.g., fairness, economics. Robustness of learning algorithms to adversarial agents. Artificial neural networks, including deep learning. High-dimensional and non-parametric statistics. Adaptive data analysis and selective inference. Learning with algebraic or combinatorial ...
its fairness. As Kiviat (2019, p. 1151) writes consistently in reference to creditscoring: “Algorithmic prediction is imbued with normative viewpoints—theyare viewpoints that suit the goals of corporations”—and the goals of securityagencies, one might add.Before this backdrop, the study of ...
(2019) explore further how machine learning can incorporate notions of fairness, justice, and due process, and suggest a shift from a solution-oriented approach to a process-oriented one in helping decision makers understand the technology they use. It is in this process-oriented approach that ...
This study identifies the roots of inequality of opportunity in South Korea by applying algorithmic approaches to survey data. In contrast to extant studies, we identify the roots of inequality of opportunity by estimating the importance of variables, in
Findable, Accessible, Interoperable, and Reusable (FAIR) data management Techniques for enabling ownership, control, and access Identifying fake news and misinformation Managing bias and ensuring fairness Enabling transparency, explainability, and accountability ...
Merely adding constraints to this optimisation in the form of technical metrics creates tensions between fairness and accuracy. This is often referred to as the ‘cost of fair classification’ or the ‘fairness accuracy trade-off’ [150, 158, 159]. Such trade-off arises when there is biased ...