  Although current deep models for face tasks surpass human performance on some
benchmarks, we do not understand how they work. Thus, we cannot predict how it
will react to novel inputs, resulting in catastrophic failures and unwanted
biases in the algorithms. Explainable AI helps bridge the gap, but currently,
there are very few visualization algorithms designed for faces. This work
undertakes a first-of-its-kind meta-analysis of explainability algorithms in
the face domain. We explore the nuances and caveats of adapting general-purpose
visualization algorithms to the face domain, illustrated by computing
visualizations on popular face models. We review existing face explainability
works and reveal valuable insights into the structure and hierarchy of face
networks. We also determine the design considerations for practical face
visualizations accessible to AI practitioners by conducting a user study on the
utility of various explainability algorithms.
