Responsibility and regulation: Exploring social measures of trust in medical AI

Glenn McGarry; Andy Crabtree; Alan Chamberlain; Lachlan Urquhart

doi:10.1145/3686038.3686041

Responsibility and regulation: Exploring social measures of trust in medical AI

Glenn McGarry
, Andy Crabtree
, Alan Chamberlain
, Lachlan Urquhart

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

Abstract

This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.

Original language	English
Title of host publication	TAS '24
Subtitle of host publication	Proceedings of the Second International Symposium on Trustworthy Autonomous Systems
Publisher	ACM Press
Pages	1-5
Number of pages	5
ISBN (Print)	9798400709890
DOIs	https://doi.org/10.1145/3686038.3686041
Publication status	Published - 16 Sept 2024
Event	Second International Symposium on Trustworthy Autonomous Systems - Austin, United States Duration: 16 Sept 2024 → 18 Sept 2024

Conference

Conference	Second International Symposium on Trustworthy Autonomous Systems
Abbreviated title	TAS' 24
Country/Territory	United States
City	Austin
Period	16/09/24 → 18/09/24

Keywords / Materials (for Non-textual outputs)

Trustworthy Autonomous System
responsible AI
ethnography
medical AI
SaMD

Access to Document

10.1145/3686038.3686041Licence: Creative Commons: Attribution (CC-BY)

McGarryEtalTAS2024ResponsibilityAndRegulationFinal published version, 148 KBLicence: Creative Commons: Attribution (CC-BY)

http://arxiv.org/abs/2408.02386v1Licence: Creative Commons: Attribution (CC-BY)

Trustworthy Autonomous Systems Regulation and Governance Node
Urquhart, L. (Principal Investigator)
1/11/20 → 30/11/24
Project: Research
Horizon: Trusted Data-Driven Products
Urquhart, L. (Principal Investigator)
University of Nottingham
1/08/20 → 7/12/25
Project: Research

Cite this

@inproceedings{c55f05bd538e4df8aafecff7fe1ba211,

title = "Responsibility and regulation: Exploring social measures of trust in medical AI",

abstract = "This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.",

keywords = "Trustworthy Autonomous System, responsible AI, ethnography, medical AI, SaMD",

author = "Glenn McGarry and Andy Crabtree and Alan Chamberlain and Lachlan Urquhart",

year = "2024",

month = sep,

day = "16",

doi = "10.1145/3686038.3686041",

language = "English",

isbn = "9798400709890",

pages = "1--5",

booktitle = "TAS '24",

publisher = "ACM Press",

note = "Second International Symposium on Trustworthy Autonomous Systems, TAS' 24 ; Conference date: 16-09-2024 Through 18-09-2024",

}

McGarry, G, Crabtree, A, Chamberlain, A & Urquhart, L 2024, Responsibility and regulation: Exploring social measures of trust in medical AI. in TAS '24: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems., 27, ACM Press, pp. 1-5, Second International Symposium on Trustworthy Autonomous Systems, Austin, Texas, United States, 16/09/24. https://doi.org/10.1145/3686038.3686041

TY - GEN

T1 - Responsibility and regulation

T2 - Second International Symposium on Trustworthy Autonomous Systems

AU - McGarry, Glenn

AU - Crabtree, Andy

AU - Chamberlain, Alan

AU - Urquhart, Lachlan

PY - 2024/9/16

Y1 - 2024/9/16

N2 - This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.

AB - This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.

KW - Trustworthy Autonomous System

KW - responsible AI

KW - ethnography

KW - medical AI

KW - SaMD

U2 - 10.1145/3686038.3686041

DO - 10.1145/3686038.3686041

M3 - Conference contribution

SN - 9798400709890

SP - 1

EP - 5

BT - TAS '24

PB - ACM Press

Y2 - 16 September 2024 through 18 September 2024

ER -

Responsibility and regulation: Exploring social measures of trust in medical AI

Abstract

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Projects

Trustworthy Autonomous Systems Regulation and Governance Node

Horizon: Trusted Data-Driven Products

Cite this