The relationship between trust in AI and trustworthy machine learning technologies

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.

Ethics and Trustworthiness of AI for Predicting the Risk of Recidivism: A Systematic Literature Review

“…It is worth noting that trust develops over time based on the trustee's behavior and conformation to a trustor's beliefs. Therefore, it is essential to understand what precedes the establishment of trust, referred to as the antecedent of trust [32,35,36,40]. The antecedents of trust are ability, benevolence, integrity, and predictability.…”

“…Before diving into what trustworthy AI entails, there is a need to understand other qualities that influence stakeholders' trust in AI systems. There are three qualities: human, environmental, and technological qualities [32]. Human qualities or attributes are associated with unique cultural backgrounds, past experiences, and ideologies.…”

“…Trustworthy AI encapsulates the must-have qualities of the AI that warrant ethical approaches [32]. A review of trustworthy AI states that incorporating trust in AI's development and design will enable stakeholders to fully realize its potential [39,42].…”

“…Several studies relating to trustworthy AI for predicting recidivism risk focused on one or more aspects of trustworthy AI. Several other studies focused on the theoretical concept of trustworthy AI, such as in [25][26][27][28][29][30][31][32][33], or focused on other domains (e.g., robotics, healthcare etc.). These theoretical reviews are discussed in detail in the related works section.…”

A Methodological Study of Human-Computer Interaction: A Review

“…HCI is the most crucial step in the design of any type of computer system since it is a crucial component of "man-machine systems" [14]. whose presence is intended to achieve a shared communication as well as the task itself to develop a shared platform between "people and machines" in order to comprehend the actions carried out by the machines, such as "creating input and output ways of information" [15,16]. The success of any interface design depends on how well it enables "communication between human and computer systems" in its entirety [17].…”