Abstract
Ethics should be a practice, not a checkbox. Data scientists want to answer questions about individuals and society using the vast torrent of data that flows around us. Machine learning practitioners want to develop and connect complex
models of the world and use them safely in critical situations. Ethical issues can be seen as getting in the way of the core idea and form pain points around managing, using and learning from data, as well as designing human-centric and ethical systems. This is because there is a design gap around ethics in data
science and machine learning: the tools that we use do not support ethical data use, which means that data scientists and machine learning practitioners, already engaged in technically complex, multidisciplinary work, must add another dimension to their thinking. This work proposes and outlines an infrastructure and framework that can support in-the-moment ethical decision
making and recording, as well as post-hoc audits and ethical model deployment.
models of the world and use them safely in critical situations. Ethical issues can be seen as getting in the way of the core idea and form pain points around managing, using and learning from data, as well as designing human-centric and ethical systems. This is because there is a design gap around ethics in data
science and machine learning: the tools that we use do not support ethical data use, which means that data scientists and machine learning practitioners, already engaged in technically complex, multidisciplinary work, must add another dimension to their thinking. This work proposes and outlines an infrastructure and framework that can support in-the-moment ethical decision
making and recording, as well as post-hoc audits and ethical model deployment.
Original language | English |
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Title of host publication | Proceedings of the IEEE 34th International Conference on Tools with Artificial Inelligence (ICTAI) 2022 |
Editors | Marek Reformat, Du Zhang, Nikolaos G. Bourbakis |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 1291-1295 |
Number of pages | 5 |
ISBN (Electronic) | 9798350397444 |
DOIs | |
Publication status | Published - 2023 |
Event | 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) Virtually 2022 - Duration: 31 Oct 2022 → 2 Nov 2022 https://ictai.computer.org/2022/ |
Publication series
Name | Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI |
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Volume | 2022-October |
ISSN (Print) | 1082-3409 |
Conference
Conference | 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) Virtually 2022 |
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Period | 31/10/22 → 2/11/22 |
Internet address |