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

by Morley, Jessica; Floridi, Luciano; Kinsey, Libby; Elhalal, Anat (2020)

Abstract

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.

Keywords

Applied Ethics, Artificial Intelligence, Data Governance, Digital Ethics, Ethics Of Ai, Governance, Machine Learning

Themes

Floridi, Ethics of AI, Automation

Links to Reference

• http://dx.doi.org/10.1007/s11948-019-00165-5
• https://www.ncbi.nlm.nih.gov/pubmed/31828533
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417387
• https://dx.doi.org/10.1007/s11948-019-00165-5

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