Abstract
In this article we present a new approach to practical artificial intelligence (AI) ethics in heavy industry, which was developed in the context of an EU Horizons 2020 multi partner project. We begin with a review of the concept of Industry 4.0, discussing the limitations of the concept, and of iterative categorization of heavy industry generally, for a practical human centered ethical approach. We then proceed to an overview of actual and potential AI ethics approaches to heavy industry, suggesting that current approaches with their emphasis on broad high-level principles are not well suited to AI ethics for industry. From there we outline our own approach in two sections. The first suggests tailoring ethics to the time and space situation of the shop floor level worker from the ground up, including giving specific and evolving ethical recommendations. The second describes the ethicist’s role as an ethical supervisor immersed in the development process and interpreting between industrial and technological (tech) development partners. In presenting our approach we draw heavily on our own experiences in applying the method in the Use Cases of our project, as examples of what can be done.
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10 September 2022
A Correction to this paper has been published: https://doi.org/10.1007/s00146-022-01552-6
Notes
One of the problems with focusing on high level charters, frameworks, etc.
The ‘committee’ here can include the group of ethicists themselves in some cases.
In the same way that a medical intern might well learn more from following a senior doctor on her rounds and participating in the diagnosis and treatment of actual hospital cases, than by reading and memorizing ‘principles’ from medical textbooks.
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This research was funded by the Université de Lorraine. AI-PROFICIENT has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 957391.
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Anderson, M.M., Fort, K. From the ground up: developing a practical ethical methodology for integrating AI into industry. AI & Soc 38, 631–645 (2023). https://doi.org/10.1007/s00146-022-01531-x
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DOI: https://doi.org/10.1007/s00146-022-01531-x