Reconfiguring Diversity and Inclusion for AI Ethics

Abstract

Activists, journalists, and scholars have long raised critical questions about the relationship between diversity, representation, and structural exclusions in data-intensive tools and services. We build on work mapping the emergent landscape of corporate AI ethics to center one outcome of these conversations: the incorporation of diversity and inclusion in corporate AI ethics activities. Using interpretive document analysis and analytic tools from the values in design field, we examine how diversity and inclusion work is articulated in public-facing AI ethics documentation produced by three companies that create application and services layer AI infrastructure: Google, Microsoft, and Salesforce.We find that as these documents make diversity and inclusion more tractable to engineers and technical clients, they reveal a drift away from civil rights justifications that resonates with the "managerialization of diversity" by corporations in the mid-1980s. The focus on technical artifacts -such as diverse and inclusive datasets -and the replacement of equity with fairness make ethical work more actionable for everyday practitioners. Yet, they appear divorced from broader DEI initiatives and relevant subject matter experts that could provide needed context to nuanced decisions around how to operationalize these values and new solutions. Finally, diversity and inclusion, as configured by engineering logic, positions firms not as "ethics owners" but as ethics allocators; while these companies claim expertise on AI ethics, the responsibility of defining who diversity and inclusion are meant to protect and where it is relevant is pushed downstream to their customers. CCS CONCEPTS• Social and professional topics → Codes of ethics; Computing / technology policy; • Computing methodologies → Artificial intelligence; • Applied computing → Law.

Food for thought: an analogy for digital sovereignty in education

“…Finally, the political realm deals with the role and impact of education, as a key pillar of democratic societies, and an essential economic element in knowledge-based societies. Discussions at this level encompass topics including the meaning of diversity and inclusion within a context of technological sophistication [19,20], or the role of education as a pillar of democratic, knowledge-based societies, considering the novelty, rapidity and, often, the opacity of the functioning of many new technological tools. This opacity is, in fact, sometimes deliberately produced, at other times derived from technical complexity, and emergently, when associated with machine learning systems, unpredictable and unintentional [21].…”

“…For instance, an emphasis on technological features could potentially divert educational actors' focus away from preserving their core values, instead prioritizing technological properties over broader educational goals. Conversely, ostensibly neutral definitions provided by technologists may inadvertently influence policy decisions and individual attitudes in non-neutral ways [2,19]. Our previous research [23] suggests that teachers who lack relevant domain knowledge (i.e., lack of understanding of the basic properties of technological design, and how technological systems are actually designed and used), conceptualize the use and impact of technology in education differently from computer scientists.…”

Tackling Language Modelling Bias in Support of Linguistic Diversity

“…Diversity can be seen as a value that is either intrinsic or instrumental (Zimmerman and Bradley, 2019). In the intrinsic version, diversity is good by and for itself, and evokes associations with pluralism, tradition, and authenticity (Vertovec, 2012;Chi et al, 2021). Following a more instrumental stance, the UNESCO 'Convention on the Protection and Promotion of the Diversity of Cultural Expressions' supports the idea of relating the preservation of linguistic diversity to values of tolerance, inclusion and dignity (UNESCO, 2005).…”

AI and ethics in business: A comprehensive review of responsible AI practices and corporate responsibility

“…In conclusion, recognizing and mitigating biases in AI algorithms are essential steps in fostering fairness and ethical deployment of AI technologies. Diverse and inclusive datasets, coupled with strategies for minimizing discriminatory outcomes, form a holistic approach to building AI systems that align with societal values and contribute to a more equitable future (Chi et al, 2021). The ongoing commitment to ethical AI practices is paramount as technology continues to shape the way we live, work, and interact with the world.…”