In healthcare, gender and sex considerations are crucial because they affect individuals' health and disease differences. Yet, most algorithms deployed in the healthcare context do not consider these aspects and do not account for bias detection. Missing these dimensions in algorithms used in medicine is a huge point of concern, as neglecting these aspects will inevitably produce far from optimal results and generate errors that may lead to misdiagnosis and potential discrimination. This paper explores how current algorithmic-based systems may reinforce gender biases and affect marginalized communities in healthcare-related applications. To do so, we bring together notions and reflections from computer science, queer media studies, and legal insights to better understand the magnitude of failing to consider gender and sex difference in the use of algorithms for medical purposes. Our goal is to illustrate the potential impact that algorithmic bias may have on inadvertent discriminatory, safety, and privacy-related concerns for patients in increasingly automated medicine. This is necessary because by rushing the deployment of AI technologies that do not account for diversity, we risk having an even more unsafe and inadequate healthcare delivery. By promoting the account for privacy, safety, diversity, and inclusion in algorithmic developments with health-related outcomes, we ultimately aim to inform the Artificial Intelligence (AI) global governance landscape and practice on the importance of integrating gender and sex considerations in the development of algorithms to avoid exacerbating existing or new prejudices.

Discrimination and bias are inherent problems of many AI applications, as seen in, for instance, face recognition systems not recognizing dark-skinned women and content moderator tools silencing drag 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-male configuration bias. 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 diversity and inclusion mean in the context of AI? This chapter reviews the literature on diversity and inclusion 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 women, the LGBTQ+ community, senior citizens, and disabled persons. Sex and gender diversity 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 sex and gender considerations in AI could pose for society, this chapter closes with proposing gendering algorithms, more diverse design teams, and more inclusive and explicit guiding policies. Overall, this chapter argues that by integrating diversity and inclusion considerations, AI systems can be created to be more attuned to all-inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies.