In this episode, we delve into the pivotal insights from the paper "Discrimination in the Age of Algorithms," which explores the dual-edged nature of algorithms in the battle against discrimination.
While the law aims to prevent discrimination, proving it can be challenging due to inherent human biases. This paper proposes that with transparent and accountable design, algorithms could not only identify but also mitigate these biases.
The authors discuss how by regulating how algorithms are developed—from data collection and objective function selection to model training—it's possible to counteract discrimination effectively. They emphasize that algorithms are not naturally objective and can indeed reinforce existing biases if the data used is biased.
Yet, they also present a method through which algorithms can help make decision-making processes more transparent and quantifiable, thus promoting equity.
For instance, in hiring practices, algorithms could be employed to pinpoint and eliminate biases related to race, gender, or criminal history. Furthermore, the paper illustrates how algorithms could advance fairness for disadvantaged groups by enhancing the accuracy of predictions in scenarios like pre-trial release decisions, where current human judgments often result in disparities.
Join us as we unpack the nuanced argument that with rigorous design and regulation, algorithms have the potential to be a transformative tool for equity.
This podcast is based on the publication "DISCRIMINATION IN THE AGE OF ALGORITHMS", Jon Kleinberg*, Jens Ludwig**, Sendhil Mullainathany and
Cass R. Sunsteinz 2019. Published by Oxford University Press on behalf of The John M. Olin Center for Law, Economics and Business at Harvard Law School:https://academic.oup.com/jla/article-abstract/doi/10.1093/jla/laz001/5476086
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.
Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.
