2023 / Didar Zowghi, Francesca da Rimini

Diversity and Inclusion in Artificial Intelligence

To date, there has been little concrete practical advice about how to ensure that diversity and inclusion considerations should be embedded within both specific Artificial Intelligence (AI) systems and the larger global AI ecosystem. In this chapter, we present a clear definition of diversity and inclusion in AI, one which positions this concept within an evolving and holistic ecosystem. We use this definition and conceptual framing to present a set of practical guidelines primarily aimed at AI technologists, data scientists and project leaders. Our focus is socio-technological rather than relying on purely technical or human factors. In this chapter, we use “socio-technical” to cover “how humans interact with technology within the broader societal context” [1]. A socio-technical perspective on diversity and inclusion in AI and the underlying issue of bias requires processes and procedures that involve stakeholders and end users, examine cultural dynamics and norms, and evaluate, monitor, and respond to societal impacts.


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