The relationship between trust in AI and trustworthy machine learning technologies

Ehsan Toreini; Mhairi Aitken; Kovila Coopamootoo; Karen Elliott; Carlos Gonzalez Zelaya; Aad van Moorsel

doi:10.1145/3351095.3372834

The relationship between trust in AI and trustworthy machine learning technologies

Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, Aad van Moorsel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

64 Citations (Scopus)

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.

Original language	English
Title of host publication	FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Publisher	Association for Computing Machinery
Pages	272-283
Number of pages	12
ISBN (Electronic)	9781450369367
DOIs	https://doi.org/10.1145/3351095.3372834
Publication status	Published - 27 Jan 2020
Event	3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020 - Barcelona, Spain Duration: 27 Jan 2020 → 30 Jan 2020

Publication series

Name	FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Conference

Conference	3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020
Country/Territory	Spain
City	Barcelona
Period	27/01/20 → 30/01/20

Bibliographical note

Funding Information:
This work was funded in part by the UK Engineering and Physical Sciences Research Council for the projects titled “Fintrust: Trust Engineering for the Financial Industry” (EP/R033595/1) and “EPSRC Centre for Doctoral Training in Cloud Computing for Big Data” (EP/L015358/1).

Publisher Copyright:
© 2020 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.

Keywords

Artificial intelligence
Machine learning
Trust
Trustworthiness

ASJC Scopus subject areas

Business, Management and Accounting(all)
Engineering(all)

Access to Document

10.1145/3351095.3372834

Cite this

Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C. G., & van Moorsel, A. (2020). The relationship between trust in AI and trustworthy machine learning technologies. In FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 272-283). (FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency). Association for Computing Machinery . https://doi.org/10.1145/3351095.3372834

Toreini, Ehsan ; Aitken, Mhairi ; Coopamootoo, Kovila et al. / The relationship between trust in AI and trustworthy machine learning technologies. FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery , 2020. pp. 272-283 (FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency).

@inproceedings{9404708d49964742a058b513ea267d0b,

title = "The relationship between trust in AI and trustworthy machine learning technologies",

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.",

keywords = "Artificial intelligence, Machine learning, Trust, Trustworthiness",

author = "Ehsan Toreini and Mhairi Aitken and Kovila Coopamootoo and Karen Elliott and Zelaya, {Carlos Gonzalez} and {van Moorsel}, Aad",

note = "Funding Information: This work was funded in part by the UK Engineering and Physical Sciences Research Council for the projects titled “Fintrust: Trust Engineering for the Financial Industry” (EP/R033595/1) and “EPSRC Centre for Doctoral Training in Cloud Computing for Big Data” (EP/L015358/1). Publisher Copyright: {\textcopyright} 2020 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.; 3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020 ; Conference date: 27-01-2020 Through 30-01-2020",

year = "2020",

month = jan,

day = "27",

doi = "10.1145/3351095.3372834",

language = "English",

series = "FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency",

publisher = "Association for Computing Machinery ",

pages = "272--283",

booktitle = "FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency",

}

Toreini, E, Aitken, M, Coopamootoo, K, Elliott, K, Zelaya, CG & van Moorsel, A 2020, The relationship between trust in AI and trustworthy machine learning technologies. in FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery , pp. 272-283, 3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020, Barcelona, Spain, 27/01/20. https://doi.org/10.1145/3351095.3372834

The relationship between trust in AI and trustworthy machine learning technologies. / Toreini, Ehsan; Aitken, Mhairi; Coopamootoo, Kovila et al.
FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery , 2020. p. 272-283 (FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - The relationship between trust in AI and trustworthy machine learning technologies

AU - Toreini, Ehsan

AU - Aitken, Mhairi

AU - Coopamootoo, Kovila

AU - Elliott, Karen

AU - Zelaya, Carlos Gonzalez

AU - van Moorsel, Aad

N1 - Funding Information: This work was funded in part by the UK Engineering and Physical Sciences Research Council for the projects titled “Fintrust: Trust Engineering for the Financial Industry” (EP/R033595/1) and “EPSRC Centre for Doctoral Training in Cloud Computing for Big Data” (EP/L015358/1). Publisher Copyright: © 2020 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.

PY - 2020/1/27

Y1 - 2020/1/27

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Machine learning

KW - Trust

KW - Trustworthiness

UR - http://www.scopus.com/inward/record.url?scp=85079632241&partnerID=8YFLogxK

U2 - 10.1145/3351095.3372834

DO - 10.1145/3351095.3372834

M3 - Conference contribution

AN - SCOPUS:85079632241

T3 - FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

SP - 272

EP - 283

BT - FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

PB - Association for Computing Machinery

T2 - 3rd ACM Conference on Fairness, Accountability, and Transparency, FAT* 2020

Y2 - 27 January 2020 through 30 January 2020

ER -

Toreini E, Aitken M, Coopamootoo K, Elliott K, Zelaya CG, van Moorsel A. The relationship between trust in AI and trustworthy machine learning technologies. In FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery . 2020. p. 272-283. (FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency). doi: 10.1145/3351095.3372834

The relationship between trust in AI and trustworthy machine learning technologies

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this