Proposed Guidelines for the Responsible Use of Explainable Machine Learning

Code

• github.com/jphall663/kdd_2019
  OfficialIn papernone★ 0
• github.com/jphall663/responsible_xai
  OfficialIn papernone★ 0
• github.com/jphall663/hc_ml
  none★ 22
• github.com/h2oai/xai_guidelines
  none★ 0
• github.com/jphall663/interpretable_machine_learning_with_python
  none★ 0

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.

Tasks

Reproductions