“…If, on the other hand, interpretability is defined as the possibility to provide a post-hoc, compact natural language explanation of why a certain output was produced in response to a certain input, then humans and complex artificial models can, in principle, be equally interpretable. Saliency maps (Simonyan, Vedaldi, & Zisserman, 2013), behavioral testing (Ribeiro, Wu, Guestrin, & Singh, 2020), probing methods (Bolukbasi, Chang, Zou, Saligrama, & Kalai, 2016;Bordia & Bowman, 2019;Gardner et al, 2020;Kim et al, 2019;Linzen & Baroni, 2020), and adversarial attacks (I. I.…”