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The Responsible Machine Learning Principles

September 9, 2020

From the Institute for Ethical AI & Machine Learning, this set of principles for the responsible development of ML systems is well worth your time. Not only is the language very easy to understand for the level of conceptual complexity, but each principle is further explored through practical, accessible, and appropriate examples. The first principle of "Human augmentation", effectively keeping a human in the review process of ML systems, is laudable and necessary, though perhaps undercut by the allowance of its temporary status. Our prevailing opinion is that an evolving model should always have a human-in-loop since models degrade and environments change. Similarly, while the value of "Reproducible operations" is recognized, reproducibility is seen as primarily a technical need. We believe that reperformance by objective, non-technical audiences creates optimal assurance and guarantee of responsible use of ML.

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Principles & Frameworks