Ethic Amanuensis: Supporting Machine Learning Practitioners Making and Recording Ethical Decisions

D.S. Murray-Rust, K. Tsiakas

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
7 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationProceedings of the IEEE 34th International Conference on Tools with Artificial Inelligence (ICTAI) 2022
EditorsMarek Reformat, Du Zhang, Nikolaos G. Bourbakis
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages1291-1295
Number of pages5
ISBN (Electronic)9798350397444
DOIs
Publication statusPublished - 2023
Event34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) Virtually 2022
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Duration: 31 Oct 20222 Nov 2022
https://ictai.computer.org/2022/

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2022-October
ISSN (Print)1082-3409

Conference

Conference34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) Virtually 2022
Period31/10/222/11/22
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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