AI for Social Good? Inspirations from Participatory Action Research (PAR) to Critical Data Studies

Medrado, A. and Verdegem, P. 2024. AI for Social Good? Inspirations from Participatory Action Research (PAR) to Critical Data Studies. London University of Westminster.

TitleAI for Social Good? Inspirations from Participatory Action Research (PAR) to Critical Data Studies
AuthorsMedrado, A. and Verdegem, P.
TypeProject report
Abstract

AI, artificial intelligence, is computer programming that learns and adapts from data. The term is used to describe any computer system that is taught to mimic intelligent human behaviours . There is a lot of hype around AI but the fact is that AI is all around us, everyday. It’s in our phones, in ride hailing apps, recommendation systems, in smart maps, smart cities, facial recognition, biometric technologies and more. A lot of research has been done on what AI can do to solve problems in areas like medicine, engineering, to tackle global challenges, such as the pandemic and climate change.

At the same time, there is another story that shows that AI technologies might increase global inequalities, contribute to environmental degradation, and are based on the exploitation of labour and extraction of resources. As such, there is growing evidence that AI is driving/intensifying a new colonial order whereby the Global North profits from the exploitation of the Global South.

So, what if AI technologies are actually worsening some of the social problems that we face? Problems like discrimination, racism, social inequalities, unemployment, precarious work?

In this project, we reflected upon these and other questions, recognising the need to think critically about:
- What type of AI do we have? And what type of AI do we want?
- How can AI be more inclusive?
- Can we speak of “AI for social good” without really knowing what it means to promote social good?

KeywordsArtificial Intelligence
Participatory Action Research
Minorities
Social Good
Year2024
PublisherUniversity of Westminster
Place of publicationLondon
Publication dates
Published09 May 2023
Page range1-12
File
File Access Level
Open (open metadata and files)

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