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\title{\bf Anthropological Arguments on the Symbolic Nature of Interpretability}
\author{
    Greg Coppola \\
    {\em coppola.ai}
}
\date{\today}

\begin{document}
\maketitle

\section{Abstract}
A common complaint with neural networks--including large language models--is that their internal {\em representation structures} are not ``human interpretable''.
This is arguably the most serious practical concern in the development of the field of {\em artificial intelligence} today.
The increasing capabilities of artificial intelligence mean that they are increasingly taking on higher levels of ``decision-making'', and indeed the goal of many right now is in fact to increase the amount and the scope of the decisions being given to fully automatic {\em agents}.

In order for machine thinking to be ``interpretable'' to humans, we must understand how humans ``interpret'' things in the first place.
In this project, we investigate the nature of the term ``interpretable'' in an interdisciplinary empirical way, drawing on insights from psychology, anthropology, language study, and other aspects of historical, empirical, human data that illustrate just what it means for a system or a message to be ``interpretable'' by humans.

We argue that, ``interpretability to humans'' is largely to be identified with the use of: {\bf symbols}, {\bf logic} and {\bf probability}, and in that order of relative importance.
In general, we argue that ``interpretability'' is a major advantage of our {\em Logical Bayesian Network} \citep{coppola2024__theory_experiments} over many other model classes.

\section{Dynamic Online Project}
The dynamically updated content—documents, code, and diagrams—for this project can be found at the {\em GitHub} project at \citep{coppola2025__interpret}.
We intend to package any applicable contributions as focused contributions to refereed papers.

\begin{thebibliography}{99}
\bibitem[Coppola, 2024]{coppola2024__theory_experiments}
Coppola, G. (2024).
\newblock The Quantified Boolean Bayesian Network: Theory and Experiments with
    a Logical Graphical Model.
\newblock arXiv preprint arXiv:2402.06557,
\newblock \url{https://arxiv.org/abs/2402.06557}.

\bibitem[Coppola, 2025]{coppola2025__interpret}
Coppola, G. (2025).
\newblock Anthropological Arguments on the Symbolic Nature of Interpretability.
\newblock GitHub repository,
\newblock \url{https://github.com/gregorycoppola/interpret}.
\end{thebibliography}
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