Diversity and Inclusion in Artificial Intelligence

Information technology and law series/Information technology & law series, pp. 109–134

Abstract

Discrimination and biasBias are inherent problems of many AI applications, as seen in, for instance, face recognition systems not recognizing dark-skinned womenWomen and content moderator tools silencing drag queensDrag queens online. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-maleMale configuration biasBias. Although being a pressing issue, understanding how AI systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy in social science and technical communities. This chapter contributes to filling this gap by exploring the research question: what do diversityDiversity and inclusionInclusion mean in the context of AI? This chapter reviews the literature on diversityDiversity and inclusionInclusion in AI to unearth the underpinnings of the topic and identify key concepts, research gaps, and evidence sources to inform practice and policymaking in this area. Here, attention is directed to three different levels of the AI development process: the technical, the community, and the target user level. The latter is expanded upon, providing concrete examples of usually overlooked communities in the development of AI, such as womenWomen, the LGBTQ+ community, senior citizens, and disabled personsDisabled persons. SexSex and genderGender diversityDiversity considerations emerge as the most at risk in AI applications and practices and thus are the focus here. To help mitigate the risks that missing sexSex and genderGender considerations in AI could pose for society, this chapter closes with proposing gendering algorithmsGendering algorithms, more diverse design teams, and more inclusive and explicit guiding policies. Overall, this chapter argues that by integrating diversityDiversity and inclusionInclusion considerations, AI systems can be created to be more attuned to all-inclusive societal needs, respect fundamental rightsFundamental rights, and represent contemporary values in modern societies.

Authors 2

1. Eduard Fosch‐Villaronga corresponding Aachen eLaw Center for Law and Digital Technologies

   Leiden University · Charles Sturt University

   Affiliation as printed

   Charles Sturt University, Bathurst, New South Wales, Australia

   eLaw Center for Law and Digital Technologies, Leiden University, Leiden, The Netherlands

2. Adam Poulsen

   Charles Sturt University

   Affiliation as printed

   Charles Sturt University, Bathurst, New South Wales, Australia