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Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
[article]
2018
arXiv
pre-print
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
arXiv:1806.07552v1
fatcat:7lq432d7tjhodmssobcoky6i44