Home > Research > Publications & Outputs > The Many Facets of Trust in AI

Research

Links

View graph of relations

The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency

Research output: Book/Report/Proceedings › Other report

Unpublished

Standard

The Many Facets of Trust in AI : Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency. / Knowles, Bran; Richards, John T.; Kroeger, Frens.

Arxiv, 2022. 13 p.

Research output: Book/Report/Proceedings › Other report

Harvard

Knowles, B, Richards, JT & Kroeger, F 2022, The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency. vol. arXiv:2208.00681, Arxiv. <https://arxiv.org/abs/2208.00681>

APA

Knowles, B., Richards, J. T., & Kroeger, F. (2022). The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency. Arxiv. https://arxiv.org/abs/2208.00681

Vancouver

Knowles B, Richards JT, Kroeger F. The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency. Arxiv, 2022. 13 p.

Author

Knowles, Bran ; Richards, John T. ; Kroeger, Frens. / The Many Facets of Trust in AI : Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency. Arxiv, 2022. 13 p.

Bibtex

@book{59d8ed3041874dea99abe55788ad8858,
title = "The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency",
abstract = "Efforts to promote fairness, accountability, and transparency are assumed to be critical in fostering Trust in AI (TAI), but extant literature is frustratingly vague regarding this 'trust'. The lack of exposition on trust itself suggests that trust is commonly understood, uncomplicated, or even uninteresting. But is it? Our analysis of TAI publications reveals numerous orientations which differ in terms of who is doing the trusting (agent), in what (object), on the basis of what (basis), in order to what (objective), and why (impact). We develop an ontology that encapsulates these key axes of difference to a) illuminate seeming inconsistencies across the literature and b) more effectively manage a dizzying number of TAI considerations. We then reflect this ontology through a corpus of publications exploring fairness, accountability, and transparency to examine the variety of ways that TAI is considered within and between these approaches to promoting trust.",
author = "Bran Knowles and Richards, {John T.} and Frens Kroeger",
year = "2022",
month = aug,
day = "2",
language = "English",
volume = "arXiv:2208.00681",
publisher = "Arxiv",

}

RIS

TY - BOOK

T1 - The Many Facets of Trust in AI

T2 - Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency

AU - Knowles, Bran

AU - Richards, John T.

AU - Kroeger, Frens

PY - 2022/8/2

Y1 - 2022/8/2

N2 - Efforts to promote fairness, accountability, and transparency are assumed to be critical in fostering Trust in AI (TAI), but extant literature is frustratingly vague regarding this 'trust'. The lack of exposition on trust itself suggests that trust is commonly understood, uncomplicated, or even uninteresting. But is it? Our analysis of TAI publications reveals numerous orientations which differ in terms of who is doing the trusting (agent), in what (object), on the basis of what (basis), in order to what (objective), and why (impact). We develop an ontology that encapsulates these key axes of difference to a) illuminate seeming inconsistencies across the literature and b) more effectively manage a dizzying number of TAI considerations. We then reflect this ontology through a corpus of publications exploring fairness, accountability, and transparency to examine the variety of ways that TAI is considered within and between these approaches to promoting trust.

AB - Efforts to promote fairness, accountability, and transparency are assumed to be critical in fostering Trust in AI (TAI), but extant literature is frustratingly vague regarding this 'trust'. The lack of exposition on trust itself suggests that trust is commonly understood, uncomplicated, or even uninteresting. But is it? Our analysis of TAI publications reveals numerous orientations which differ in terms of who is doing the trusting (agent), in what (object), on the basis of what (basis), in order to what (objective), and why (impact). We develop an ontology that encapsulates these key axes of difference to a) illuminate seeming inconsistencies across the literature and b) more effectively manage a dizzying number of TAI considerations. We then reflect this ontology through a corpus of publications exploring fairness, accountability, and transparency to examine the variety of ways that TAI is considered within and between these approaches to promoting trust.

M3 - Other report

VL - arXiv:2208.00681

BT - The Many Facets of Trust in AI

PB - Arxiv

ER -