Google Brain says we urgently need a Research Framework around the field of interpretability

In a recent interview Been KIM from the Google Brain team emphasizes the signficance of research in explainable AI, the importance of Human-Computer Interaction (HCI) for Artificial Intelligence generally and Machine Learning specifically (see the differences between AI and ML here), and the urgent need of an research framework around the field of interpretability. Listen to the episode six of season four of Talking Machines by Katherine GORMAN and Neil LAWRENCE here (Start at approx. 26:00):

Been KIM is a research scientist at the Google Brain team and is interested in designing machine learning methods that  make sense to humans. Her current focus is building interpretability methods for already-trained models (e.g., high performance neural networks). In particular, she believes that the language of explanations should include higher-level, human-friendly concepts.  Been gave a tutorial on explainable AI at ICML 2017 and recently the group published the paper: Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman & Finale Doshi-Velez 2018. How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. arXiv:1802.00682.