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The relationship between trust in AI and trustworthy machine learning technologies

Lookup NU author(s): Dr Ehsan Toreini, Dr Mhairi Aitken, Dr Kovila Coopamootoo, Dr Karen Elliott, Professor Aad van Moorsel

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by ACM, 2020.

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Abstract

To design and develop AI-based systems that users and the larger public can justifiably trust, one needs to understand how machine learning technologies impact trust. To guide the design and implementation of trusted AI-based systems, this paper provides a systematic approach to relate considerations about trust from the social sciences to trustworthiness technologies proposed for AI-based services and products. We start from the ABI+ (Ability, Benevolence, Integrity, Predictability) framework augmented with a recently proposed mapping of ABI+ on qualities of technologies that support trust. We consider four categories of trustworthiness technologies for machine learning, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these support the required qualities. Moreover, trust can be impacted throughout the life cycle of AI-based systems, and we therefore introduce the concept of Chain of Trust to discuss trustworthiness technologies in all stages of the life cycle. In so doing we establish the ways in which machine learning technologies support trusted AI-based systems. Finally, 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.


Publication metadata

Author(s): Toreini E, Aitken M, Coopamootoo K, Elliott K, Zelaya CG, van Moorsel A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: FAT* '20: 2020 Conference on Fairness, Accountability, and Transparency

Year of Conference: 2020

Pages: 272-283

Online publication date: 27/01/2020

Acceptance date: 01/12/2019

Date deposited: 02/03/2020

ISSN: 9781450369367

Publisher: ACM

URL: https://doi.org/10.1145/3351095.3372834

DOI: 10.1145/3351095.3372834


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