The unseen Black faces of AI algorithms

An audit of commercial facial-analysis tools found that dark-skinned faces are misclassified at a much higher rate than are faces from any other group. Four years on, the study is shaping research, regulation and commercial practices.

Data sets are essential for training and validating machine-learning algorithms. But these data are typically sourced from the Internet, so they encode all the stereotypes, inequalities and power asymmetries that exist in society. These biases are exacerbated by the algorithmic systems that use them, which means that the output of the systems is discriminatory by nature, and will remain problematic and potentially harmful until the data sets are audited and somehow corrected. Although this has long been the case, the first major steps towards overcoming the issue were taken only four years ago, when Joy Buolamwini and Timnit Gebru¹ published a report that kick-started sweeping changes in the ethics of artificial intelligence (AI).

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Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.