Indirect Discrimination and the Duty to Avoid Compounding Injustice

By Deborah Hellman. 2017

Subfields: AI Ethics, Fairness & Society, Philosophy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

1. Hanna M. Wallach
2. Adam Tauman Kalai
3. Krishnaram Kenthapadi
4. Sahin Cem Geyik
5. Toniann Pitassi
6. Richard S. Zemel
7. Solon Barocas
8. Elliot Creager

In Philosophy

1. Richard S. Zemel
2. Hanna M. Wallach
3. Adam Tauman Kalai
4. Solon Barocas
5. Jennifer Wortman Vaughan
6. Jörn-Henrik Jacobsen

Citations in the corpus

• Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “parities involving these groups may compound existing injustices (Hellman 2018).”
• Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting (2019, background): “tion. □ As explained in Section 2, if the initial gender imbalance is due to prior injustices, an occupation classifier will compound these injustices, which may correspond to indirect discrimination [26]. It is clear from Figure 3 that there are few occupations with an equal percentage of men and w”
• Flexibly Fair Representation Learning by Disentanglement (2019, background): “These outcomes are potentially unethical or illegal (Barocas & Selbst, 2016; Hellman, 2018), and behoove researchers to investigate more equitable and robust models.”