3 research outputs found
On the Art and Science of Machine Learning Explanations
This text discusses several popular explanatory methods that go beyond the
error measurements and plots traditionally used to assess machine learning
models. Some of the explanatory methods are accepted tools of the trade while
others are rigorously derived and backed by long-standing theory. The methods,
decision tree surrogate models, individual conditional expectation (ICE) plots,
local interpretable model-agnostic explanations (LIME), partial dependence
plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable
application domain. Along with descriptions of these methods, this text
presents real-world usage recommendations supported by a use case and public,
in-depth software examples for reproducibility.Comment: This manuscript is a preprint of the text for an invited talk at the
2019 KDD XAI workshop. A previous version has also appeared in the
proceedings of the Joint Statistical Meetings. Errata and updates available
here: https://github.com/jphall663/kdd_2019. Version 2 incorporated reviewer
feedback. Version 3 includes a minor adjustment to Figure 1. Version 4
corrects a minor typ
Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)
This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
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.Comment: Errata and updates available here:
https://github.com/jphall663/responsible_xa