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

This research paper focuses on how to close the gap between principles and practices in AI. The authors argue that constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline is crucial.

Researchers, Jessica Morley, Luciano Floridi, Libby Kinsey and Anat Elhalal released the applied AI ethics full typology, which is intended to eventually be an online searchable database where developers can look for appropriate tools and methodologies. This might be a tool enabling developers to shift from a prescriptive ‘ethics-by-design’ approach to a dialogic, pro-ethical design approach.

More importantly, the researchers concluded that we need to move from a state of ‘short-termism’, where short-term, commercial incentives to invest in ethics of AI are still not encouraged. A longer-term and sector-wide perspective in terms of return on investment in ethics needs to be encouraged. The incentive for ‘AI ethics’ is directly related to the increase in confidence from investors and research funders. The opposite is also true, and a delay in recognising the importance of ethics in AI can result in a stagnation when it comes to the development of artificial intelligence.