Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Abstract
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.
Cite
@article{arxiv.1806.07552,
title = {Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems},
author = {Richard Tomsett and Dave Braines and Dan Harborne and Alun Preece and Supriyo Chakraborty},
journal= {arXiv preprint arXiv:1806.07552},
year = {2018}
}
Comments
presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden