AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability

Aihua Yan; David Xu

AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Personal healthcare information (PHI) disclosure is vital in leveraging artificial intelligence (AI) technology for depression treatment. Two challenges for PHI disclosure are high privacy concern and low trust. In this study, we integrate three theoretical lenses, i.e., information boundary theory, trust, and AI principles to investigate whether AI principles of empathy, accountability, and explainability can address these two challenges. We propose that AI empathy can increase depression patients’ privacy concern and trust simultaneously. This paradox of high privacy concern and high trust has to be addressed for successful AI deployment in depression treatment. The proxies of AI accountability such as AI company reputation and government regulation can help reduce this paradox. Further, we argue that explainability can moderate the relationships between this paradox (i.e., privacy concern and trust) and patient’s intention to disclose PHI. Overall, our expected results can provide significant implications to IS literature and practitioners.

Original language	English
Title of host publication	ICIS 2021 Proceedings
Publisher	Association for Information Systems
ISBN (Print)	978-1-7336325-9-1
Publication status	Published - 2021
Event	42nd International Conference on Information Systems (ICIS 2021): Building Sustainability and Resilience with IS: A Call for Action - Austin, United States Duration: 12 Dec 2021 → 15 Dec 2021 https://icis2021.aisconferences.org/

Conference

Conference	42nd International Conference on Information Systems (ICIS 2021)
Country/Territory	United States
City	Austin
Period	12/12/21 → 15/12/21
Internet address	https://icis2021.aisconferences.org/

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

accountability
artificial intelligence
depression treatment
empathy
explainability
privacy concern
trust

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability",

abstract = "Personal healthcare information (PHI) disclosure is vital in leveraging artificial intelligence (AI) technology for depression treatment. Two challenges for PHI disclosure are high privacy concern and low trust. In this study, we integrate three theoretical lenses, i.e., information boundary theory, trust, and AI principles to investigate whether AI principles of empathy, accountability, and explainability can address these two challenges. We propose that AI empathy can increase depression patients{\textquoteright} privacy concern and trust simultaneously. This paradox of high privacy concern and high trust has to be addressed for successful AI deployment in depression treatment. The proxies of AI accountability such as AI company reputation and government regulation can help reduce this paradox. Further, we argue that explainability can moderate the relationships between this paradox (i.e., privacy concern and trust) and patient{\textquoteright}s intention to disclose PHI. Overall, our expected results can provide significant implications to IS literature and practitioners.",

keywords = "accountability, artificial intelligence, depression treatment, empathy, explainability, privacy concern, trust",

author = "Aihua Yan and David Xu",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 42nd International Conference on Information Systems (ICIS 2021) : Building Sustainability and Resilience with IS: A Call for Action ; Conference date: 12-12-2021 Through 15-12-2021",

year = "2021",

language = "English",

isbn = "978-1-7336325-9-1",

booktitle = "ICIS 2021 Proceedings",

publisher = "Association for Information Systems",

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}

Yan, A & Xu, D 2021, AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability. in ICIS 2021 Proceedings. Association for Information Systems, 42nd International Conference on Information Systems (ICIS 2021), Austin, Texas, United States, 12/12/21. <https://aisel.aisnet.org/icis2021/is_health/is_health/15/>

TY - GEN

T1 - AI for Depression Treatment

T2 - 42nd International Conference on Information Systems (ICIS 2021)

AU - Yan, Aihua

AU - Xu, David

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2021

Y1 - 2021

N2 - Personal healthcare information (PHI) disclosure is vital in leveraging artificial intelligence (AI) technology for depression treatment. Two challenges for PHI disclosure are high privacy concern and low trust. In this study, we integrate three theoretical lenses, i.e., information boundary theory, trust, and AI principles to investigate whether AI principles of empathy, accountability, and explainability can address these two challenges. We propose that AI empathy can increase depression patients’ privacy concern and trust simultaneously. This paradox of high privacy concern and high trust has to be addressed for successful AI deployment in depression treatment. The proxies of AI accountability such as AI company reputation and government regulation can help reduce this paradox. Further, we argue that explainability can moderate the relationships between this paradox (i.e., privacy concern and trust) and patient’s intention to disclose PHI. Overall, our expected results can provide significant implications to IS literature and practitioners.

AB - Personal healthcare information (PHI) disclosure is vital in leveraging artificial intelligence (AI) technology for depression treatment. Two challenges for PHI disclosure are high privacy concern and low trust. In this study, we integrate three theoretical lenses, i.e., information boundary theory, trust, and AI principles to investigate whether AI principles of empathy, accountability, and explainability can address these two challenges. We propose that AI empathy can increase depression patients’ privacy concern and trust simultaneously. This paradox of high privacy concern and high trust has to be addressed for successful AI deployment in depression treatment. The proxies of AI accountability such as AI company reputation and government regulation can help reduce this paradox. Further, we argue that explainability can moderate the relationships between this paradox (i.e., privacy concern and trust) and patient’s intention to disclose PHI. Overall, our expected results can provide significant implications to IS literature and practitioners.

KW - accountability

KW - artificial intelligence

KW - depression treatment

KW - empathy

KW - explainability

KW - privacy concern

KW - trust

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85189189852&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-7336325-9-1

BT - ICIS 2021 Proceedings

PB - Association for Information Systems

Y2 - 12 December 2021 through 15 December 2021

ER -

AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability

Abstract

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Bibliographical note

Research Keywords

UN SDGs

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