Skip down to main content

Producing cross-disciplinary insights for diverse and inclusive design to AI in arts and fashion

Mobile phone taking pictures in art gallery

Producing cross-disciplinary insights for diverse and inclusive design to AI in arts and fashion

Full project title: Producing cross-disciplinary insights for diverse and inclusive design to AI in arts and fashion

Overview

Practice-based approaches to AI – in which artists, fashion designers, and computer scientists experiment with the inherent limitations of algorithmic reasoning – allow us to reflect on the social experiences mediated by technology. For instance, artists are interrogating AI to explore algorithmic reasoning, such as bias in the training data concerning gender and sexual identity (Jake Elwes and Edinburgh Futures Institute, 2020).

While practice-based insights can highlight the limitations of AI systems, it is unclear how important issues of diversity and inclusivity can be operationalised to inform the regulation of these tools and practices. Given the speed of advances in AI techniques, including the rise of Generative Adversarial Networks and Large Language Models for the generation of content, the need to delimit “harmful” and “non-harmful” uses requires a multidisciplinary approach to the design, use and regulation of these technologies.

The project will draw on two workshops involving (1) legal scholars, social scientists, and AI ethicists; and (2) artists, fashion designers and computer scientists, to produce a training guide for artists, fashion designers, and computer scientists engaging with practice-based approaches using AI. It will examine how practice-based approaches can reveal regulatory tensions concerning algorithmic bias, and how a socio-legal approach to AI can feed back into practice-based approaches, to promote diverse and inclusive design of AI systems.

Key Information

Funders:
  • OUP John Fell Fund
  • British Irish Law Education and Technology Association (BILETA) research grant
  • Project dates:
    August 2023 - April 2024

    Related Topics:

    Privacy Overview
    Oxford Internet Institute

    This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

    Strictly Necessary Cookies
    • moove_gdrp_popup -  a cookie that saves your preferences for cookie settings. Without this cookie, the screen offering you cookie options will appear on every page you visit.

    This cookie remains on your computer for 365 days, but you can adjust your preferences at any time by clicking on the "Cookie settings" link in the website footer.

    Please note that if you visit the Oxford University website, any cookies you accept there will appear on our site here too, this being a subdomain. To control them, you must change your cookie preferences on the main University website.

    Google Analytics

    This website uses Google Tags and Google Analytics to collect anonymised information such as the number of visitors to the site, and the most popular pages. Keeping these cookies enabled helps the OII improve our website.

    Enabling this option will allow cookies from:

    • Google Analytics - tracking visits to the ox.ac.uk and oii.ox.ac.uk domains

    These cookies will remain on your website for 365 days, but you can edit your cookie preferences at any time via the "Cookie Settings" button in the website footer.