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Responsible AI for labour market equality (BIAS)

Konnikov, A; Rets, I; Hughes, KD; Al-Ani, JA; Denier, N; Ding, L; Hu, S; ... Yu, D; + view all (2022) Responsible AI for labour market equality (BIAS). In: Hantrais, Linda, (ed.) How to Manage International Multidisciplinary Research Projects. (pp. 75-87). Elgar Green open access

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Abstract

This case study focusses on the BIAS project, an interdisciplinary and international collaboration between researchers in Canada and the UK, investigating Responsible AI for labour market equality. The project was funded by the UK Economic and Social Research Council (ESRC) and the Social Sciences and Humanities Research Council of Canada (SSHRC) under the Canada-UK Artificial Intelligence Initiative. Drawing on interviews with the founding team, and a survey with all team members, this case study examines how the core project team managed the research process. It illustrates the challenges of collaborating and decision-making in a highly diverse team, and the value of adopting an egalitarian approach to team management, based on flexible mindsets, and an openness towards disciplinary differences. The case study analyses the strategies developed to ensure effective communication across disciplinary and cultural boundaries. The discussion highlights the lessons learnt, and the practical solutions and rewards of intra- and inter-disciplinary work.

Type: Book chapter
Title: Responsible AI for labour market equality (BIAS)
ISBN-13: 9781802204711
Open access status: An open access version is available from UCL Discovery
DOI: 10.4337/9781802204728.00014
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute
URI: https://discovery.ucl.ac.uk/id/eprint/10210772
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