Training in Co-Creation as a Methodological Approach to Improve AI Fairness

Ian Slesinger (), Evren Yalaz, Stavroula Rizou, Marta Gibin, Emmanouil Krasanakis and Symeon Papadopoulos
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Ian Slesinger: Trilateral Research, London SW1X 7QA, UK
Evren Yalaz: Trilateral Research, London SW1X 7QA, UK
Stavroula Rizou: Centre for Research & Technology Hellas (CERTH), 57001 Thermi, Greece
Marta Gibin: Department of Sociology and Economic Law, University of Bologna, 40126 Bologna, Italy
Emmanouil Krasanakis: Centre for Research & Technology Hellas (CERTH), 57001 Thermi, Greece
Symeon Papadopoulos: Centre for Research & Technology Hellas (CERTH), 57001 Thermi, Greece

Societies, 2024, vol. 14, issue 12, 1-19

Abstract: Participatory design (PD) and co-creation (Co-C) approaches to building Artificial Intelligence (AI) systems have become increasingly popular exercises for ensuring greater social inclusion and fairness in technological transformation by accounting for the experiences of vulnerable or disadvantaged social groups; however, such design work is challenging in practice, partly because of the inaccessible domain of technical expertise inherent to AI design. This paper evaluates a methodological approach to make addressing AI bias more accessible by incorporating a training component on AI bias in a Co-C process with vulnerable and marginalized participant groups. This was applied by socio-technical researchers involved in creating an AI bias mitigation developer toolkit. This paper’s analysis emphasizes that critical reflection on how to use training in Co-C appropriately and how such training should be designed and implemented is necessary to ensure training allows for a genuinely more inclusive approach to AI systems design when those most at risk of being adversely affected by AI technologies are often not the intended end-users of said technologies. This is acutely relevant as Co-C exercises are increasingly used to demonstrate regulatory compliance and ethical practice by powerful institutions and actors developing AI systems, particularly in the ethical and regulatory environment coalescing around the European Union’s recent AI Act.

Keywords: participatory design; co-creation; AI bias; AI fairness; social inclusion; AI ethics; AI regulation; training (search for similar items in EconPapers)
JEL-codes: A13 A14 P P0 P1 P2 P3 P4 P5 Z1 (search for similar items in EconPapers)
Date: 2024
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