From Tools to Toolkits: Towards more Reusable, Composable, and Reliable Machine Learning Interpretability
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Arvind Satyanarayan's keynote at Visualization in Data Science (VDS) 2021, held at ACM KDD 2021. visualdatascience.org/2021/program/.
As machine learning models are increasingly deployed into real-world contexts, the need for interpretability grows more urgent. In order to hold models accountable for the outcomes they produce, we cannot rely on quantitative measures of accuracy alone; rather, we also need to be able to qualitatively inspect how they operate. To meet this challenge, recent years have seen an explosion of research developing techniques and systems to interpret model behavior. But, are we making meaningful progress on this issue? In this talk, I will give us a language for answering this question by drawing on frameworks in human-computer interaction (HCI) and by analogizing to the progress of research in data visualization. I will use this language to characterize existing work (including work my research group is currently conducting) and sketch out directions for future work.
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