Work

Beyond the algorithm: rethinking the network account of trustworthy ai through lexical threshold-based multidimensional utility analysis

Year: 2026

Type: article

Abstract: In this paper, we present an overview of the main conceptual framework for trustworthy AI and argue that the network account offers a superior alternative. We identify and illustrate the possible nodes within an AI network, specify the key attributes each node should possess, and clarify what constitutes a sufficient threshold for each attribute. Building on this foundation, we introduce a novel framework for the network account of trustworthy AI: Lexical Threshold-Based Multidimensional Utility Theory (LTMU). This framework lexically ranks trust-relevant attributes, establishes minimum thresholds for each and assesses each attribute with a distinct utility scale. We then demonstrate how the LTMU framework can be applied to analyse two exceptional cases in which trust-relevant attributes come into conflict in different ways, arguing that its application yields reasonable and defensible outcomes in both scenarios. Supplementary Information: The online version contains supplementary material available at 10.1007/s00146-026-02909-x.

Source: AI & Society

DOI: 10.1007/s00146-026-02909-x

Authors: Fei Song, Julian Savulescu, Michael Dunn

Institutions: National University of Singapore, University of Oxford

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,190