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

Abstract

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Wiener, 1960) (Samuel, 1960). 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. Therefore, 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 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.

The Ethics of AI Ethics: An Evaluation of Guidelines

“…In order to analyze all this in sufficient depth, ethics has to partially transform to "microethics". This means that at certain points, a substantial change in the level of abstraction has to happen insofar as ethics aims to have a certain impact and influence in the technical disciplines and the practice of research and development of artificial intelligence (Morley et al 2019). On the way from ethics to "microethics", a transformation from ethics to technology ethics, to machine ethics, to computer ethics, to information ethics, to data ethics has to take place.…”

Principles alone cannot guarantee ethical AI

“…Prior work points to the difficulty of embedding ethical values and principles in technology design and the development cycle 1,56,58,59 . Many such methods exist 60 , including participatory design, reflective design, Values at Play and value-sensitive design 1 , but thus far they have largely been implemented and studied in academic contexts, which are more receptive to normative concerns than commercial settings 22,60,61 . Value-conscious methods are also largely procedural, not functional.…”

AI Ethics – Too Principled to Fail?

“…15 48 Many such methods exist, ix including participatory design, reflective design, Values@Play, and Value-Sensitive Design, 1 but thus far they have largely been implemented and studied in academic contexts which are more receptive to normative concerns than commercial settings. 21,49,50 Value-conscious methods are also largely procedural, not functional. Generally speaking, they introduce values, normative issues, and relevant stakeholders into the development process.…”

“…55-57 A recent ix Morley et al provide an up-to-date overview of tools, methods, and research to translate AI Ethics principles into practice. 49 x Other value-specific methods, such as 'privacy by design' or 'security by design' are more successful in this regard, in part because they address technically tractable normative concepts. xi While contract law can provide legal accountability in some cases, it is not directly comparable to medical malpractice or tort which define negligence against a heightened 'professional standard of care' to be upheld by individual practitioners.…”