The relationship between trust in AI and trustworthy machine learning technologies

TL;DR

This paper explores how different machine learning technologies influence trust in AI systems by linking social science trust concepts with technological qualities across the system's lifecycle.

Contribution

It introduces a systematic approach to relate trust concepts with AI technologies, focusing on FEAS categories and the Chain of Trust in AI lifecycle stages.

Findings

FEAS technologies relate to trust qualities and policy frameworks.

Trust can be maintained or eroded at different lifecycle stages.

A new framework links social trust concepts with AI technological qualities.

Abstract

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.