Equal Protection Under the Algorithm : A Legal-Inspired Framework for Identifying Discrimination in Machine Learning

By Sucheta Soundarajan, Daniel L. Clausen. 2018

How researchers see this paper

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

1. Ryan Shaun Joazeiro de Baker
2. Brian Magerko
3. Aaron Hawn
4. Duri Long
5. Jinhee Kim
6. Cristóbal Romero
7. R. Luckin
8. Davy Tsz Kit Ng

Citations in the corpus

• Algorithmic Bias in Education (2021, background): “Similarly, Soundarajan and Clausen (2018) describe how the legal definition of a protected class rests in part on its “immutability,” the degree to which an individual can move in and out of the suggested class.”
• What is AI Literacy? Competencies and Design Considerations (2020, background): “Bias/fairness: Most of the papers in the 2018 FAT ML conference focused on issues related to algorithmic bias (e.g. [118,121]).”