Guidelines for Responsible and Human Centered Use of Explainable Machine Learning
Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and white hat hacking and forensic analysis of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML is also an important, mandatory, or embedded aspect of commercial predictive modeling in industries like financial services. However, like many technologies, explainable ML can be misused, particularly as a faulty safeguard for harmful black boxes, e.g. fairwashing, and for other malevolent purposes like model stealing [1] [31] [34] This text presents several definitions, examples, and qualifications in Section 2 before covering the details of responsible and human centered use guidelines in Sections 3.1 3.4. This text concludes in Section 4 with the seemingly natural argument for a holistic approach to ML that includes interpretable (i.e. white box models along with explanatory, debugging, and disparate impact analysis techniques for any ML system that impacts humans.
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