Can machines be uncertain?

TL;DR

This paper explores how AI systems can represent and realize different types of uncertainty, including epistemic and subjective, using various architectures and conceptual frameworks.

Contribution

It introduces a novel perspective that some uncertainty states are interrogative attitudes, framing uncertainty as questions rather than propositions.

Findings

Different architectures accommodate uncertainty in distinct ways

Distinction between epistemic and subjective uncertainty clarified

Uncertainty states can be viewed as interrogative attitudes

Abstract

The paper investigates whether and how AI systems can realize states of uncertainty. By adopting a functionalist and behavioral perspective, it examines how symbolic, connectionist and hybrid architectures make room for uncertainty. The paper distinguishes between epistemic uncertainty, or uncertainty inherent in the data or information, and subjective uncertainty, or the system's own attitude of being uncertain. It further distinguishes between distributed and discrete realizations of subjective uncertainty. A key contribution is the idea that some states of uncertainty are interrogative attitudes whose content is a question rather than a proposition.