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AI ethics from the ground up: cultivating interdisciplinary capabilities (for care)

O'Donovan, C; (2020) AI ethics from the ground up: cultivating interdisciplinary capabilities (for care). Presented at: 12th ACM Web Science Conference 2020 (WebSci '20), Southampton, UK. Green open access

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Abstract

Interdisciplinary research has led to significant breakthroughs in the fields of AI, robotics and autonomous systems. Yet it has also been the basis of significant controversies. Cambridge Analytica and Facebook's amalgam of psychographics, data science and engineering at scale being perhaps the most infamous recent example. Despite continued promotion from research funders, it should be clear then that interdisciplinary research is not in itself an uncontested good. This presentation aims to explore the political and ethical dimensions of interdisciplinary research practices in the fields of AI, robotics and autonomous systems. Following a human capabilities approach and using mixed-methods, we locate and map a set of research capabilities valued by researchers at the Bristol Robotics Laboratory. This prompts a discussion on what it means to consider ethics of AI, not from a set of normative statements, but as practiced on the ground.

Type: Conference item (Presentation)
Title: AI ethics from the ground up: cultivating interdisciplinary capabilities (for care)
Event: 12th ACM Web Science Conference 2020 (WebSci '20)
Location: Southampton, UK
Dates: 07 - 10 July 2020
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.1145/3394332.3402894
Language: English
Keywords: robotics, human capabilities, interdisciplinarity, innovation, science policy, artificial intelligence
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Science and Technology Studies
URI: https://discovery.ucl.ac.uk/id/eprint/10104664
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