Developing a Human Centred AI Masters: the Good, the Bad and the Ugly
Authors: 
Barry Feeney, Monica Zuccarini, Tarry Singh, Huib Aldewereld, Stefano Marrone, 
Keith QuilleAuthors Info & Claims
Pages 660 - 661
Abstract
The increasing use of AI in industry and society not only expects but demands that we build human-centred competencies into our AI education programmes. The computing education community needs to adapt, and while the adoption of standalone ethics modules into AI programmes or the inclusion of ethical content into traditional applied AI modules is progressing, it is not enough. To foster student competencies to create AI innovations that respect and support the protection of individual rights and society, a novel ground-up approach is needed. This panel presents on one such approach, the development of a Human-Centred AI Masters (HCAIM) as well as the insights and lessons learned from the process. In particular, we discuss the design decisions that have led to the multi-institutional master's programme. Moreover, this panel allows for discussion on pedagogical and methodological approaches, content knowledge areas and the delivery of such a novel programme, along with challenges faced, to inform and learn from other educators that are considering developing such programmes.
References
[1]
Human centered artificial intelligence masters (hcaim), cef-tc programme, european platform for digital skills and jobs: https://www.humancenteredai. eu/project.html. 2022.
[2]
De Donato, F. Flammini, R. M. Goverde, Z. Lin, R. Liu, S. Marrone, R. Nardone, T. Tang, and V. Vittorini. Artificial intelligence in railway transport: Taxonomy, regulations and applications. IEEE Transactions on Intelligent Transportation Systems, 2021.
[3]
A.-M. Creu, F. Monti, S. Marrone, X. Dong, M. Bronstein, and Y.-A. de Montjoye. Interaction data are identifiable even across long periods of time. Nature Communications, 13(1):1--11, 2022.
[4]
K. Quille and S. Bergin. Cs1: how will they do? how can we help? a decade of research and practice. Computer Science Education, 29(2--3):254--282, 2019.
[5]
K. Quille, S. Nam Liao, E. Costelloe, K. Nolan, A. Mooney, and K. Shah. Press: Predicting student success early in cs1. a pilot international replication and generalization study. In Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education Vol 1, ITiCSE 2022, New York, NY, USA, 2022. Association for Computing Machinery.
[6]
K. Quille and K. Nolan. Predicting success in cs1 - an open access data project. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2, SIGCSE 2022, page 1126, New York, NY, USA, 2022. Association for Computing Machinery.
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C. Sannino, M. Gravina, S. Marrone, G. Fiameni, and C. Sansone. Lessonable: Leveraging deep fakes in mooc content creation. In International Conference on Image Analysis and Processing. Springer, 2022.
Index Terms
- Developing a Human Centred AI Masters: the Good, the Bad and the Ugly
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Information
Published In
July 2022
686 pages
ISBN:9781450392006
DOI:10.1145/3502717
- General Chairs:
- Brett A. Becker,
- Keith Quille,
- Mikko-Jussi Laakso,
- Program Chairs:
- Erik Barendsen,
- Simon
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Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 07 July 2022
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- EU via the CEF-TC programme
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ITiCSE 2022
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ITiCSE 2022: Innovation and Technology in Computer Science Education
July 8 - 13, 2022
Dublin, Ireland
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