Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture

By Selena Silva, Martin Kenney, S. Superiore, Sant’Anna. 2018

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “Several recent papers have demonstrated how social goals are, sometimes clumsily, abstracted and formulated to fit into a prediction task [23, 114, 48, 49, 94, 29, 113, 32, 100].”
Measuring Bias (2023, background): “In order to find the best predictive models, some simplification assumptions are made which often do not hold in real applications [32].”
Algorithmic Bias in Education (2021, background): “Common sources of bias have been outlined in several taxonomies and related to stages in the machine learning lifecycle (Barocas et al., 2019; Hellström et al., 2020; Mehrabi et al., 2019; Silva & Kenney, 2018; Suresh & Guttag, 2020).”
Algorithmic management in a work context (2021, background): “One approach is the algorithmic audit, which is predicated on the idea that algorithmic biases may produce a record that can be read, understood, and altered in future iterations (Diakopoulos, 2016; Silva and Kenney, 2018).”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “Several recent papers have demonstrated how policy goals are, sometimes clumsily, abstracted and formulated to fit into a prediction task (Dobbe et al. 2018, Eckhouse et al. 2018, Green 2018, Green &Hu 2018,Ochigame et al. 2018, Silva & Kenney 2018, Passi & Barocas 2019, Selbst et al. 2019).”
A Framework for Understanding Unintended Consequences of Machine Learning (2019, background): “ugh the lens of American anti-discrimination law, and Danks and London (2017) present a taxonomy of “algorithmic bias,” focusing on its manifestation in autonomous systems. The latter was extended by Silva and Kenney (2018) to address racial bias in decision-making more generally, though categories ”