Concrete ethical guidelines and best practices in machine learning development

Abstract

The rapid adoption of Machine Learning (ML) in human’s daily lives and activities is raising ethical dilemmas and issues. A form of minimizing possible harm to society is to provide guidance to ML developers, who can build systems that are ethical by design. Unfortunately, developers do not have proper ethical formation in regular undergraduate courses, and the existing documents, despite being abundant, are vague and focused on governments and corporations rather than on individual developers. This paper proposes ethical recommendations, 18 concrete guidelines and 24 best practices, for developers. These recommendations were formulated in a focus group and validated quantitatively in a survey with over 130 ML developers working in both industry and Academia. This paper also investigates the state of adoption of such recommendations and compares what developers think they should do to achieve more ethical results versus what they actually do.

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