Defining Diversity and Inclusion in AI

Table of Links

Abstract, Impact Statement, and Introduction

Defining Diversity and Inclusion in AI

Research Motivation

Research Methodology

Results

Discussion

Conclusion and Future Work and References

II. DEFINING DIVERSITY AND INCLUSION IN AI

Despite the acknowledged importance of diversity and inclusion, there is a gap in the literature regarding how these principles can be practically implemented in AI systems. FoschVillaronga and Poulsen [15], define D&I in AI as a multifaceted concept that addresses both AI's technical and sociocultural aspects. They highlight diversity as the representation of individuals concerning socio-political power differentials such as gender and race. Inclusion, they suggest, is the representation of an individual user within a set of instances, with better alignment between a user and the options relevant to them, indicating greater inclusion. This concept is further analyzed at three levels: the technical, the community, and the user. The technical level considers whether algorithms account for all necessary variables and if they classify users in a discriminatory manner. The community level examines diversity and inclusivity in AI development teams, looking at gender representation and diversity of backgrounds. Finally, the user level focuses on the intended users of the system and how the research and implementation process takes into account the stakeholders and their feedback, emphasizing the principles of Responsible Research and Innovation.

The paucity of a comprehensive definition for D&I in AI within the existing literature has motivated us to propose a normative definition and a set of guidelines for ensuring these principles are incorporated into the AI development process. We have sought and received feedback iteratively on the definition and guidelines from Responsible AI and D&I experts [16]. We focused on a socio-technological perspective, recognizing that addressing bias and unfairness requires a holistic approach that considers cultural dynamics and norms and involves end users and other stakeholders. We defined D&I in AI as: ‘inclusion’ of humans with ‘diverse’ attributes and perspectives in the data, process, system, and governance of the AI ecosystem. Diversity refers to the representation of the differences in attributes of humans in a group or society. Attributes are known facets of diversity, including (but not limited to) the protected attributes in Article 26 of the International Covenant on Civil and Political Rights (ICCPR), as well as race, color, sex, language, religion, national or social origin, property, birth or other status, and inter-sections of these attributes. Inclusion is the process of proactively involving and representing the most relevant humans with diverse attributes; those who are impacted by, and have an impact on, the AI ecosystem context.

We proposed that diversity and inclusion in AI can be structured and conceptualized involving five pillars: humans, data, process, system, and governance. The humans pillar focuses on the importance of including individuals with diverse attributes in all stages of AI development. The data pillar highlights the need to be mindful of potential biases in data collection and use. The process pillar emphasizes the need for diversity and inclusion considerations during the development, deployment, and evolution of AI systems. The system pillar recognizes the necessity for the AI system to be tested and monitored to ensure it does not promote non-inclusive behaviors. The governance pillar underlines the importance of structures and processes that ensure AI development is compliant with ethical principles, laws, and regulations. AI ecosystem refersto the 5 pillars (humans, data, process, system, and governance), plus the environment (i.e. application domain), within which the AI system is deployed and used.