Twenty-Fifth International Conference on Diversity in Organizations, Communities & Nations

Abstract

The focus of this paper is on inclusion, questioning the status quo of knowledge production and thus emphasising the possible diverse contributions to AI development. When AI collects data associated with humans, it becomes a mirror that reflects the stereotypes and inequalities that humanity continues to suffer from. These biases, scientists argue, are often due to the lack of diversity in the people tasked with developing AI systems and tools, as well as biases within science and culture: indeed, the teams working in the field of AI often have cultural gaps that are inevitably reflected in the AI tools they produce. This paper argues that the voices and characteristics of people who speak a different social language and think and act in ways that differ from the status quo must be included. This means taking the development of AI out of the realm of the current cycle of knowledge production and enabling new developers and engineers to adapt tools to the needs and experiences of their diverse communities through the collaboration of different disciplines, cultures, scientific fields and approaches. The thesis of this paper is that fostering a deeper and more substantive dialogue between the social sciences - especially sociology, education and psychology - is an essential support for developing a science that is inclusive from the outset and at the design stage.