Ban the Box, Criminal Records, and Statistical Discrimination: A Field Experiment

By Amanda Y. Agan, Sonja B. Starr. 2016

148 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/59dfc5bc345e790cab3cdb9a8107dec6e6569aba

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Citations in the corpus

Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning (2018, background): “Specifically, we observe that while humans tend to have a good understanding of feature volitionality, reliability, privacy, and relevance in a decision making scenario, they tend to be bad at predicting what impact using a feature might have on the decision outcomes (Agan and Starr 2016).”
A Few Bad Apples? Racial Bias in Policing (2017, background): “Popular approaches include audit studies that vary individual race (Bertrand and Mullainathan, 2004; Edelman et al., 2017; Agan and Starr, 2016), studies that vary the observability of race or gender (Goldin and Rouse, 2000; Grogger and Ridgeway, 2006; Donohue, 2014), and studies of settings with ri”
Collateral Consequences of Punishment: A Critical Review and Path Forward (2018, background): “In a field experiment of the efficacy of ban the box, Agan & Starr (2017) find support for Holzer et al.’s (2006) contention.”
Discrimination in the Age of Algorithms (2018, background): “…backgrounds with their willingness to hire black applicants, and for studies showing that implementation of a “ban the box” policy that restricts employers from asking about prior record seems to reduce their willingness to hire black applicants, see Agan & Starr (2018) and Doleac & Hansen (2016).”
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction (2018, background): “Given their potential impact on human lives, concerns have been raised about the fairness of the decisions made by algorithms [1, 5, 7, 22].”
Human Decision Making with Machine Assistance (2019, background): “Prior work has chiefly been interested in the fairness of decisions completely delegated to machines [1, 4, 6, 31] and has extensively studied the distributive [26, 29, 39, 43, 49, 50, 56, 65, 73–75] and procedural [36, 38] fairness of machine decision-making [9, 14, 18, 19, 32, 46], as well as the…”