Journal article
Maximizing team synergy in AI-related interdisciplinary groups: an interdisciplinary-by-design iterative methodology
- Abstract:
- In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our exercise lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: (i) definition of the hypothesis space, (ii) building-up of a common lexicon, (iii) scenario-building, (iv) interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 636.1KB, Terms of use)
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- Publisher copy:
- 10.1007/s00146-022-01518-8
Authors
+ Sant'Anna School of Advanced Studies
More from this funder
- Funder identifier:
- https://ror.org/025602r80
- Publisher:
- Springer
- Journal:
- AI and Society More from this journal
- Volume:
- 38
- Issue:
- 4
- Pages:
- 1443-1452
- Publication date:
- 2022-06-28
- Acceptance date:
- 2022-06-01
- DOI:
- EISSN:
-
1435-5655
- ISSN:
-
0951-5666
- Language:
-
English
- Keywords:
- Pubs id:
-
2376662
- Local pid:
-
pubs:2376662
- Source identifiers:
-
W4283662687
- Deposit date:
-
2026-08-11
- ARK identifier:
Terms of use
- Copyright holder:
- Bisconti et al.
- Copyright date:
- 2022
- Rights statement:
- © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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