Professor Gwen Seabourne
Honorary Professor
Marie Louise Kinsler KC
Honorary Professor
Paul Reid KC
Honorary Fellow
Malcolm Graham KPM
Honorary Fellow
Producing cross-disciplinary insights for diverse and inclusive design to AI in arts and fashion
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This project aims to investigate the extent to which one can use practice-based approaced to AI to facilitate the analysis of socio-legal issues and regulations surrounding AI bias.
Professor Burkhard Schafer, Professor of Computational Legal Theory at Edinburgh Law School, and Dr Daria Onitiu worked on this project that was funded by the British and Irish Law Education and Technology Association (BILETA) research award, as well as the Oxford University Press John Fell Fund.
Practice-based approaches to Artificial Intelligence (AI) - in which artists, fashion designers, and computer scientists experiment with the inherent limitations of algorithmic reasoning - allow us to reflect on the issues of bias, fairness, diversity, and inclusivity mediated by technology. Through two workshops involving (1) legal scholars, social scientists, and AI ethicists; and (2) artists, fashion designers, and computer scientists, this report investigates the extent to which we can use practice-based approaches to AI to facilitate the analysis of socio-legal issues and regulation surrounding AI bias.
This work highlights the role of understanding AI as a socio-technical challenge in creative arts, and the role of practice-based approaches to negotiate principles of fairness, diversity, and inclusion. To integrate socio-legal concepts related to bias, fairness, inclusivity, and diversity into the creative discourse on AI, it is necessary to uncover a range of preconceptions held by key players - developers, providers, and Big Tech - about AI's impact on creative practices. Understanding these preconceptions is crucial for developing a socio-technical approach to AI design, use, and governance.
The guide makes three key recommendations on how the negotiating function in practice-based approaches can be formalised for policy. These entail:
- Understanding AI as an umbrella term that can shape, manipulate, and deceive an inclusive, diverse approach to the design of AI.
- Noting the relevance of language framing, which is a narrative that encapsulates certain design and normative choices on how developers operationalise notions surrounding diversity and bias, cultural blindsides, whilst revealing the different incentives for undermining responsible and accountable design and deployment of advanced AI.
- Defining how practice-based approaches can challenge implicit, explicit assumptions about responsible AI and promote a new interrogatory mindset regarding the design and deployment of AI technology.
Read and download the full report [PDF]
