It’s not the algorithm behaving badly, but how we define fairness that determines an artificial intelligence system’s impact. Bias is often identified as one of the major risks associated with artificial intelligence (AI) systems. Recently reported cases of known bias in AI — racism in the criminal justice system, gender discrimination in hiring — are undeniably worrisome. The public discussion about bias in such scenarios often assigns blame to the algorithm itself. The algorithm, it is said, has made the wrong decision, to the detriment of a particular group. But this claim fails to take into account the human component: People perceive bias through the subjective lens of fairness. This is the subject of this article.
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