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\begin{document}

\title{Abstraction, Validation, and Generalization for Explainable Artificial
Intelligence}



\author[1]{Scott Cheng-Hsin Yang}%
\author[1]{Tomas Folke}%
\author[1]{Patrick Shafto}%
\affil[1]{Rutgers University Newark}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
Neural network architectures are achieving superhuman performance on an
expanding range of tasks. To effectively and safely deploy these
systems, their decision-making must to be understandable to a wide range
of stakeholders. Methods to explain AI have been proposed to answer this
challenge, but a lack of theory impedes the development of systematic
abstractions which are necessary for cumulative knowledge gains. We
propose Bayesian Teaching as a framework for unifying explainable AI
(XAI) by integrating machine learning and human learning. Bayesian
Teaching formalizes explanation as a communication act of an explainer
to shift the beliefs of an explainee. This formalization decomposes any
XAI method into four components: (1) the inference to be explained, (2)
the explanatory medium, (3) the explainee model, and (4) the explainer
model. The abstraction afforded by Bayesian Teaching to decompose any
XAI method elucidates the invariances among them. The decomposition of
XAI systems enables modular validation, as each of the first three
components listed can be tested semi-independently. This decomposition
also promotes generalization through recombination of components from
different XAI systems, which facilitates the generation of novel
variants. These new variants need not be evaluated one by one provided
that each component has been validated, leading to an exponential
decrease in development time. Finally, by making the goal of explanation
explicit, Bayesian Teaching helps developers to assess how suitable an
XAI system is for its intended real-world use case. Thus, Bayesian
Teaching provides a theoretical framework that encourages systematic,
scientific investigation of XAI.%
\end{abstract}%



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