AI Algorithm Transparency: Pipes for Trust, Not Prisms - Mitigating General Negative Attitudes Towards AI and Enhancing Trust

Keon Young Park; Ho Young Yoon

AI Algorithm Transparency: Pipes for Trust, Not Prisms - Mitigating General Negative Attitudes Towards AI and Enhancing Trust

Keon Young Park^*, Ho Young Yoon

^*Corresponding author for this work

Department of Communication Studies

Research output: Contribution to conference › Conference paper › peer-review

Abstract

This study examines how AI algorithm transparency can mitigate negative attitudes and enhance trust in AI systems and parent companies. We conducted an online experiment using a 2 (AI algorithm transparency High vs. Low) by 2 (Issue involvement High vs. Low) between-subject design. Results showed AI algorithm transparency mitigates the negative relationship between negative attitude and trust in the parent company, especially when issue involvement was high.

Original language	English
Publication status	Published - Aug 2024
Event	Association for Education in Journalism and Mass Communication (AEJMC) 2024 107th Annual Conference: Representation and Voice — The Future of Democracy - Philadelphia Marriott Downtown, Philadelphia, United States Duration: 8 Aug 2024 → 11 Aug 2024 https://community.aejmc.org/conference/home (Link to conference website) https://community.aejmc.org/conference/schedule/program (Link to conference programme) https://community.aejmc.org/conference/paper-competition/paperabstracts (Link to conference paper abstract)

Conference

Conference	Association for Education in Journalism and Mass Communication (AEJMC) 2024 107th Annual Conference
Country/Territory	United States
City	Philadelphia
Period	8/08/24 → 11/08/24
Internet address	https://community.aejmc.org/conference/home (Link to conference website) https://community.aejmc.org/conference/schedule/program (Link to conference programme) https://community.aejmc.org/conference/paper-competition/paperabstracts (Link to conference paper abstract)

Cite this

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title = "AI Algorithm Transparency: Pipes for Trust, Not Prisms - Mitigating General Negative Attitudes Towards AI and Enhancing Trust",

abstract = "This study examines how AI algorithm transparency can mitigate negative attitudes and enhance trust in AI systems and parent companies. We conducted an online experiment using a 2 (AI algorithm transparency High vs. Low) by 2 (Issue involvement High vs. Low) between-subject design. Results showed AI algorithm transparency mitigates the negative relationship between negative attitude and trust in the parent company, especially when issue involvement was high.",

author = "Park, {Keon Young} and Yoon, {Ho Young}",

year = "2024",

month = aug,

language = "English",

note = "Association for Education in Journalism and Mass Communication (AEJMC) 2024 107th Annual Conference : Representation and Voice — The Future of Democracy ; Conference date: 08-08-2024 Through 11-08-2024",

url = "https://community.aejmc.org/conference/home, https://community.aejmc.org/conference/schedule/program, https://community.aejmc.org/conference/paper-competition/paperabstracts",

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AI Algorithm Transparency: Pipes for Trust, Not Prisms - Mitigating General Negative Attitudes Towards AI and Enhancing Trust. / Park, Keon Young; Yoon, Ho Young.
2024. Paper presented at Association for Education in Journalism and Mass Communication (AEJMC) 2024 107th Annual Conference, Philadelphia, Pennsylvania, United States.

Research output: Contribution to conference › Conference paper › peer-review

TY - CONF

T1 - AI Algorithm Transparency: Pipes for Trust, Not Prisms - Mitigating General Negative Attitudes Towards AI and Enhancing Trust

AU - Park, Keon Young

AU - Yoon, Ho Young

PY - 2024/8

Y1 - 2024/8

N2 - This study examines how AI algorithm transparency can mitigate negative attitudes and enhance trust in AI systems and parent companies. We conducted an online experiment using a 2 (AI algorithm transparency High vs. Low) by 2 (Issue involvement High vs. Low) between-subject design. Results showed AI algorithm transparency mitigates the negative relationship between negative attitude and trust in the parent company, especially when issue involvement was high.

AB - This study examines how AI algorithm transparency can mitigate negative attitudes and enhance trust in AI systems and parent companies. We conducted an online experiment using a 2 (AI algorithm transparency High vs. Low) by 2 (Issue involvement High vs. Low) between-subject design. Results showed AI algorithm transparency mitigates the negative relationship between negative attitude and trust in the parent company, especially when issue involvement was high.

M3 - Conference paper

T2 - Association for Education in Journalism and Mass Communication (AEJMC) 2024 107th Annual Conference

Y2 - 8 August 2024 through 11 August 2024

ER -

AI Algorithm Transparency: Pipes for Trust, Not Prisms - Mitigating General Negative Attitudes Towards AI and Enhancing Trust

Abstract

Conference

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