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Category: Analysis

Description: This repositories stores a supplemental file with further evidence and references for a viewpoint article. In the viewpoint article, we suggest that social biases are exacerbated by the lack of diversity in the AI field. These biases cannot be effectively addressed by technical solutions that aim at mitigating biases stemming from data sources and data processing or from the algorithm itself. We argue that a social view—which has been neglected in AI development so far—is needed to address the root causes of some biases, given that AI systems are often reflections of our social structures. While great technical progress has been made in measuring and testing fairness and mitigating unfairness, biases may originate from any stage of AI development through the developers involved. As a result, some AI system biases reflect the social biases present within the AI developers that build them. Hence, we argue that the lack of diversity in AI development is a source of social biases. As a solution, we present a set of practical recommendations that empower organizations to increase diversity in AI development.