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.

本研究提供了一种系统的方法来将社会科学信任概念与建立在人工智能服务和产品上使用的机器学习技术相关联，探讨了四种类别的机器学习技术 (公平性、可解释性、可审计性和安全性) 如何影响人们的信任，并介绍了在 AI 系统生命周期的不同阶段建立信任的技术需求。