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.

翻译：尽管当前用于人脸任务的深度模型在某些基准测试中超越了人类表现，但我们并不理解其工作机制。因此，我们无法预测模型对新输入的反应，导致算法中出现灾难性故障和不期望的偏见。可解释人工智能有助于弥合这一差距，但目前专门为人脸设计的可视化算法十分稀缺。本文首次对人脸领域的可解释性算法进行了元分析。我们探讨了将通用可视化算法适配到人脸领域的细微差别和注意事项，并通过在流行的人脸模型上计算可视化结果进行说明。我们回顾了现有的人脸可解释性研究工作，揭示了对人脸网络结构与层级关系的宝贵见解。此外，我们通过开展一项关于不同可解释性算法实用性的用户研究，为人工智能从业者确定了实用的人脸可视化设计考量。