Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability

By Jon M. Kleinberg, Sendhil Mullainathan. 2018

76 citations recorded by the source corpus.

Algorithms can be a powerful aid to decision-making - particularly when decisions rely, even implicitly, on predictions [7]. We are already seeing algorithms play this role in domains including hiring, education, lending, medicine, and criminal justice [2, 6, 10]. As is typical in machine learning applications, accuracy is an important measure for these tasks.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Measurement and Fairness (2019, background): “ribute to the meaning of the test scores" [44]. Indeed, interacting with measurements can create room for more individual judgments, automation bias, and (lack of) intuition and interpretability [6, 16, 28, 38, 55, 60]. That is, as measurements themselves may change the world in possibly uninte”
Manipulating and Measuring Model Interpretability (2018, background): “Third, people may be worried that models might behave in ways that are unfair [22, 33, 54, 80, 91, 106, 109].”
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI (2020, background): “Accountability Auditability of algorithms/data/design Factsheets (lineage) Minimize and report negative impacts Fairness checklists Acknowledge and evaluate trade-offs Reporting the robustness-accuracy [1] trade-off or simplicity-equity trade-off [37] Ensure redress Fairness checklists -”