Some Policy Recommendations to Fight Gender and Racial Biases in AI


  • Galit Wellner Tel Aviv University, Faculty of Humanities



Algorithms, Artificial Intelligence, Gender and Racial Bias, Machine Learning, Transparency


Many solutions have been proposed to fight the problem of bias in AI. The paper arranges them into five categories: (a) "no gender or race" - ignoring and omitting any reference to gender and race from the dataset; (b) transparency - revealing the considerations that led the algorithm to reach a certain conclusion; (c) designing algorithms that are not biased; (d) "machine education" that complements "machine learning" by adding value sensitivity to the algorithm; or (e) involving humans in the process. The paper will selectively provide policy recommendations to promote the solutions of transparency (b) and human-in-the-loop (e). For transparency, the policy can be inspired by the measures implemented in the pharmaceutical industry for drug approval. To promote human-in-the-loop, the paper proposes an "ombudsman" mechanism that ensures the biases detected by the users are dealt with by the companies who develop and run the algorithms.


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How to Cite

Wellner, Galit. 2022. “Some Policy Recommendations to Fight Gender and Racial Biases in AI”. The International Review of Information Ethics 32 (1). Edmonton, Canada.