Fair Algorithms for Machine Learning

By Michael Kearns. 2017

17 citations recorded by the source corpus.

Subfields: Safety Research

How researchers see this paper

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

In Safety Research

1. Nicolas Papernot
2. Patrick Drew McDaniel
3. Nicholas Carlini
4. Ian J. Goodfellow

Citations in the corpus

• Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning (2018, methodology): “application of the DkNN: fairness in ML [93].”
• SoK: Security and Privacy in Machine Learning (2018, background): “These guarantees express a particular definition of fairness: e.g., equal or unbiased treatment [105].”
• Machine Learning for the Developing World (2018, background): “An emerging methodological field focused on fairness, accountability, and transparency in machine learning (Hardt et al. 2016; Kearns 2017; Zemel et al. 2013) is well situated to aid these systems.”