Techniques for Discrimination-Free Predictive Models

By Faisal Kamiran, Toon Calders, Mykola Pechenizkiy. 2013

35 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Political Science and International Relations, 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 Artificial Intelligence

1. Reuben Binns
2. Michael Veale

In Political Science and International Relations

1. Reuben Binns
2. Michael Veale
3. Emilio Ferrara

In Safety Research

1. Reuben Binns
2. Michael Veale
3. Emilio Ferrara
4. Jennifer Wortman Vaughan
5. Hanna M. Wallach

Citations in the corpus

• Fairness in Machine Learning: Lessons from Political Philosophy (2017, background): “Cognisant of this problem, a burgeoning research paradigm of ‘discrimination-aware data mining’ and ‘fair machine learning’ has emerged, which attempts to detect and mitigate unfairness Hajian and Domingo-Ferrer (2013); Kamiran et al. (2013); Barocas and Selbst (2016).”
• Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, methodology): “Some harms surround fairness, as it proves to be difficult to make systems that do not exhibit bias, indirectly or in subsets of data (Hajian, 2013; Kamiran et al., 2012; Barocas and Selbst, 2016).”