There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.
At the intersection of human robot interaction and story-telling is verbalization; generating explanations for human-robot interaction @cite_61 . Similar approaches are found in abductive reasoning; using a case-based model @cite_60 or explanatory coherence @cite_40 . This is also a well-studied field in brain and cognitive science by filling in the gaps of knowledge by imagining new ideas @cite_38 or using statistical approaches @cite_54 .
@cite_61 @cite_38 @cite_60 @cite_54 @cite_40
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Autonomous mobile robots navigate in our spaces by planning and executing routes to destinations. When a mobile robot appears at a location, there is no clear way to understand what navigational path the robot planned and experienced just by looking at it. In this work, we address the generation of narrations of autonomous mobile robot navigation experiences. We contribute the concept of verbalization as a parallel to the well-studied concept of visualization. Through verbalizations, robots can describe through language what they experience, in particular in their paths. For every executed path, we consider many possible verbalizations that could be generated. We introduce the verbalization space that covers the variability of utterances that the robot may use to narrate its experience to different humans. We present an algorithm for segmenting a path and mapping each segment to an utterance, as a function of the desired point in the verbalization space, and demonstrate its application using our mobile service robot moving in our buildings. We believe our verbalization space and algorithm are applicable to different narrative aspects for many mobile robots, including autonomous cars. Abstract A variety of theories have been put forth to explain the function of imagination, most notably that imagination engages and develops children's theory of mind and counterfactual reasoning. Here, we propose that a primary role for imagination is as a cognitive mechanism for efficiently generating new ideas without observing new evidence. Learners must generate hypotheses before they can assess the truth of these hypotheses. Given infinite possibilities, how do learners constrain the process of hypothesis generation? We suggest that learners represent abstract criteria for the solution to a problem and generate solutions that, if true, would solve the problem. As a preliminary test of this idea, we show that, in the absence of any fact of the matter (i.e., when neither prior knowledge nor statistical data distinguishes competing hypotheses), 4–6-year-olds (mean: 63 months) systematically converge on solutions to problems, consistent with an ability to imagine the abstract properties of causal problems and their solutions. Many abductive understanding systems explain novel situations by a chaining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive hypotheses are focused by specific explanations of prior episodes and by goal-based criteria reflecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday domains. We identify five central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in domains that are complex and imperfectly understood. Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others' goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind.