NEWS10 August 2020

New funding for racial justice AI and data projects

AI Data analytics News UK

UK – The Ada Lovelace Institute’s JUST AI programme has launched a £40,000 fund, supported by the Arts and Humanities Research Council (AHRC), for work addressing racial justice and AI ethics.

Ethics dictionary reference

The research institute has issued a call for proposals for projects to contribute to locating and filling gaps in ethical thinking about data and AI.

At least four grants of £10,000 each will be awarded through the funding, with applications open to those with relevant research or policy experience and creative practitioners.

The JUST AI (Joining Up Society and Technology in AI) programme, set up in 2019 by the AHRC and the Ada Lovelace Institute, aims to better understand the AI ethics field and develop a network of scholars and practitioners working in the space.

Research from the programme found that the most frequently cited papers in AI ethics in the UK refer primarily to ethical frameworks developed from the positions of European and North American philosophy. 

Successful applicants will gain affiliation with the Ada Lovelace Institute as JUST AI visiting fellows for six months.

Carly Kind, director of the Ada Lovelace Institute, said: “Through this inaugural cohort of visiting fellows at the Ada Lovelace Institute, supported by AHRC, we will be able to diversify the range of voices contributing to the development of research and understanding of the interplay between racial justice, data and AI.”

Alison Powell, director of the JUST AI network and associate professor at the London School of Economics, said: “Current research on AI ethics in the UK has tended both to prioritise established voices and sustain anti-Blackness within the AI field, and there is evidence that biases accumulate around voices that are most prominent within published work. 

“This fund will go some way towards starting to redress this current imbalance and help support and foreground scholars, creatives and ethical perspectives that have been absent or less fully considered from research and policy around data and AI.”