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From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Science and engineering ethics, 2020-08, Vol.26 (4), p.2141-2168 [Peer Reviewed Journal]

The Author(s) 2019 ;The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1353-3452 ;EISSN: 1471-5546 ;DOI: 10.1007/s11948-019-00165-5 ;PMID: 31828533

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  • Title:
    From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices
  • Author: Morley, Jessica ; Floridi, Luciano ; Kinsey, Libby ; Elhalal, Anat
  • Subjects: Artificial intelligence ; Autonomy ; Biomedical Engineering and Bioengineering ; Cybernetics ; Education ; Engineering ; Ethics ; Learning algorithms ; Machine learning ; Medicine/Public Health ; Neural networks ; Original Research/Scholarship ; Philosophy ; Philosophy of Science ; Principles ; Research methodology ; Typology
  • Is Part Of: Science and engineering ethics, 2020-08, Vol.26 (4), p.2141-2168
  • Description: The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741–742, 1960. https://doi.org/10.1126/science.132.3429.741 ; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the ‘what’ of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)—rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.
  • Publisher: Dordrecht: Springer Netherlands
  • Language: English
  • Identifier: ISSN: 1353-3452
    EISSN: 1471-5546
    DOI: 10.1007/s11948-019-00165-5
    PMID: 31828533
  • Source: Springer Nature OA/Free Journals
    ProQuest Central
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