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

TL;DR

This paper reviews existing AI ethics tools and methods, aiming to bridge the gap between ethical principles and practical implementation in machine learning development.

Contribution

It introduces a typology to help developers apply ethics throughout the AI pipeline and identifies areas needing further research.

Findings

Initial typology of AI ethics tools and methods

Identification of gaps between principles and practices

Guidance for integrating ethics in ML development

Abstract

The debate about the ethical implications of Artificial Intelligence dates from the 1960s. However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such debate has primarily focused on principles - the what of AI ethics - 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.