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

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.