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

Abstract

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.

Coproduction mechanisms to weave Indigenous knowledge, artificial intelligence, and technical data to enable Indigenous-led adaptive decision making: lessons from Australia’s joint managed Kakadu National Park

“…Elsewhere, there has been some progress in considering the ethical requirements for data and data analytical systems to be trusted and trustworthy by decision makers. This included consideration of best-practice data collection, curation, algorithm design, and analytics (e.g., Kwok 2019, Morley et al 2021). However, Indigenous leaders highlighted that less is known about if and how these ethical principles correlated with Indigenous knowledge governance systems and ethical standards (Abdilla 2018, Walter andSuina 2018).…”

When performance is not enough—A multidisciplinary view on clinical decision support

“…It has become standard practice for better AI decision support systems to claim that they are aligned with the concept of trustworthy AI. Numerous criteria have been developed for this purpose, for which various reviews are now available [ 40 , 41 ]. If one also takes the frame of the High-Level Expert Group on Artificial Intelligence: Ethics guidelines as a reference, one can say: Any AI-System must be compatible with applicable laws, meet ethical standards, and not entail unforeseen side effects [ 42 ].…”

Implementing AI Ethics: Making Sense of the Ethical Requirements

“…These components correspond to the technical robustness and safety components, and privacy and data components in the ethics guidelines for trustworthy AI. Morley et al [33] constructed a typology of methods and practices to assist developers in implementing AI ethics at each stage of machine learning development. While the list is extensive, the practices are somewhat consigned to the micro or development level.…”