Trustworthiness in Stochastic Systems: Towards Opening the Black Box

TL;DR

This paper explores how stochasticity in AI systems affects trustworthiness, proposing new models to better assess and calibrate trust in complex, probabilistic AI behaviors.

Contribution

It introduces a novel definition of stochasticity and latent value modeling to improve understanding and management of trust in stochastic AI systems.

Findings

Traditional approaches to managing stochasticity are insufficient.

A new framework for assessing trustworthiness considering stochasticity is proposed.

Foundations for more precise trust calibration in AI systems are laid.

Abstract

AI systems are increasingly tasked to complete responsibilities with decreasing oversight. This delegation requires users to accept certain risks, typically mitigated by perceived or actual alignment of values between humans and AI, leading to confidence that the system will act as intended. However, stochastic behavior by an AI system threatens to undermine alignment and potential trust. In this work, we take a philosophical perspective to the tension and potential conflict between stochasticity and trustworthiness. We demonstrate how stochasticity complicates traditional methods of establishing trust and evaluate two extant approaches to managing it: (1) eliminating user-facing stochasticity to create deterministic experiences, and (2) allowing users to independently control tolerances for stochasticity. We argue that both approaches are insufficient, as not all forms of stochasticity affect trustworthiness in the same way or to the same degree. Instead, we introduce a novel definition of stochasticity and propose latent value modeling for both AI systems and users to better assess alignment. This work lays a foundational step toward understanding how and when stochasticity impacts trustworthiness, enabling more precise trust calibration in complex AI systems, and underscoring the importance of sociotechnical analyses to effectively address these challenges.