Economics, Fairness and Algorithmic Bias

By Bo Cowgill, Catherine E. Tucker. 2020

150 citations recorded by the source corpus.

We develop an economic perspective on algorithmic fairness and the surrounding empirical, theoretical and policy issues. Our perspective draws from clear parallels between algorithms and issues in economics of discrimination, crime, personnel and technological innovation; as well as more subtle connections to environmental economics, product safety regulation, behavioral economics and economics of information. We highlight the distinction between biased algorithmic predictions and biased algorithmic objectives. We conclude by discussing economic issues in policy policy and managerial practices around reducing algorithmic unfairness.

Subfields: Economics and Econometrics, Safety Research

How researchers see this paper

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

1. Omar Badri
2. Felix Lau
3. Matthew Groh
4. Caleb Harris
5. Rachel Han
6. Luis R. Soenksen
7. Aerin Kim
8. Arash Koochek

Citations in the corpus

• Actionable Recourse in Linear Classification (2018, neutral): “These include: inverse classification, which aims to determine how the inputs to a model can be manipulated to obtain a desired outcome [1, 10]; strategic classification, which considers how to build classifiers that are robust to malicious manipulation [13, 16, 21, 22, 30]; adversarial perturbation”
• Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset (2021, background): “By creating transparency and explicitly identifying likely sources of bias, it is possible to develop machine learning models that are not only accurate but also serve as discrimination detectors [41, 32, 19].”
• Time and the Value of Data (2020, unknown): “…been examined by several authors [e.g., Aghion et al. (2019),Agrawal et al. (2019),Bald-win (2019),Brynjolfsson et al. (2018),Cockburn et al. (2019),Cowgill and Tucker (2020),Jones and Tonetti (2020),Korinek and Stiglitz (2019),Milgrom and Tadelis (2019),Reimers and Shiller (2018),Tirole…”