The goal of explainable Artificial Intelligence (XAI) is to generate
human-interpretable explanations, but there are no computationally precise
theories of how humans interpret AI generated explanations. The lack of theory
means that validation of XAI must be done empirically, on a case-by-case basis,
which prevents systematic theory-building in XAI. We propose a psychological
theory of how humans draw conclusions from saliency maps, the most common form
of XAI explanation, which for the first time allows for precise prediction of
explainee inference conditioned on explanation. Our theory posits that absent
explanation humans expect the AI to make similar decisions to themselves, and
that they interpret an explanation by comparison to the explanations they
themselves would give. Comparison is formalized via Shepard's universal law of
generalization in a similarity space, a classic theory from cognitive science.
A pre-registered user study on AI image classifications with saliency map
explanations demonstrate that our theory quantitatively matches participants'
predictions of the AI.

研究人员提出了一种心理学理论，解释了人们如何从显著性图中得出结论，为 XAI 中的精确预测和验证提供了理论依据，并进行了一项用户研究以验证该理论。

解释性的心理学理论

A Psychological Theory of Explainability

Active research pertaining to the affective phenomenon of empathy and
distress is invaluable for improving human-machine interaction. Predicting
intensities of such complex emotions from textual data is difficult, as these
constructs are deeply rooted in the psychological theory. Consequently, for
better prediction, it becomes imperative to take into account ancillary factors
such as the psychological test scores, demographic features, underlying latent
primitive emotions, along with the text's undertone and its psychological
complexity. This paper proffers team PVG's solution to the WASSA 2021 Shared
Task on Predicting Empathy and Emotion in Reaction to News Stories. Leveraging
the textual data, demographic features, psychological test score, and the
intrinsic interdependencies of primitive emotions and empathy, we propose a
multi-input, multi-task framework for the task of empathy score prediction.
Here, the empathy score prediction is considered the primary task, while
emotion and empathy classification are considered secondary auxiliary tasks.
For the distress score prediction task, the system is further boosted by the
addition of lexical features. Our submission ranked 1$^{st}$ based on the
average correlation (0.545) as well as the distress correlation (0.574), and
2$^{nd}$ for the empathy Pearson correlation (0.517).

本研究使用多输入、多任务框架，对情感评分、情感和共情分类的 WASSA 2021 共享任务进行了解决方案，并将文本数据、心理测验得分、人口统计特征和原始情绪及共情的内在相互依赖性纳入考虑范畴，进而在困难的文本数据中预测了这些情绪的强度。