Proposed Guidelines for the Responsible Use of Explainable Machine Learning

Abstract

Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits 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 or scaffolding, and for other malevolent purposes like stealing models and sensitive training data. To promote best-practice discussions for this already in-flight technology, this short text presents internal definitions and a few examples before covering the proposed guidelines. This text concludes with a seemingly natural argument for the use of interpretable models and explanatory, debugging, and disparate impact testing methods in life- or mission-critical ML systems.

arXiv commentErrata and updates available here: https://github.com/jphall663/responsible_xai

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Cited by in corpus (4)

• The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
• Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI
• Interpreting Deep Neural Networks Through Variable Importance
• Safety design concepts for statistical machine learning components toward accordance with functional safety standards