Abstract
The ability of artificial intelligence systems to offer explanations for its decisions is central to building user confidence and structuring smart human-machine interactions. Expressing the rationale behind such a system’s output is an important aspect of human-machine interaction as AI continues to be prominent in general, everyday use-cases. In this paper, we introduce a novel framework integrating Grenander’s pattern theory structures to produce inherently explainable, symbolic representations for activity interpretations. These representations provide semantically rich and coherent interpretations of video activity using connected structures of detected (grounded) concepts, such as objects and actions, that are bound by semantics through background concepts not directly observed, i.e. contextualization cues. We use contextualization cues to establish semantic relationships among concepts to infer a deeper interpretation of events than what can be directly sensed. We propose the use of six questions that can be used to gain insight into the models ability to justify its decision and enhance its ability to interact with humans. The six questions are designed to (1) build an understanding of how the model is able to infer interpretations, (2) enable us to walk through its decision-making process, and (3) understand its drawbacks and possibly address them. We demonstrate the viability of this idea on video data using a dialog model that uses interpretations to generate explanations grounded in both video data and semantics.
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Acknowledgements
This research was supported in part by NSF grants IIS 1217676 and CNS-1513126. The authors would also like to thank Daniel Sawyer for his invaluable insights during discussion.
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Aakur, S.N., de Souza, F.D.M., Sarkar, S. (2018). On the Inherent Explainability of Pattern Theory-Based Video Event Interpretations. In: Escalante, H., et al. Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-98131-4_11
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DOI: https://doi.org/10.1007/978-3-319-98131-4_11
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