Some Policy Recommendations to Fight Gender and Racial Biases in AI
DOI :
https://doi.org/10.29173/irie497Mots-clés :
Algorithms, Artificial Intelligence, Gender and Racial Bias, Machine Learning, TransparencyRésumé
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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