research-article

Experiential AI

Authors:

Dave Murray-Rust,

Michael Rovatsos,

Frank BrozAuthors Info & Claims

AI Matters, Volume 5, Issue 1

Pages 25 - 31

https://doi.org/10.1145/3320254.3320264

Published: 22 April 2019 Publication History

Abstract

Experiential AI is proposed as a new research agenda in which artists and scientists come together to dispel the mystery of algorithms and make their mechanisms vividly apparent. It addresses the challenge of finding novel ways of opening up the field of artificial intelligence to greater transparency and collaboration between human and machine. The hypothesis is that art can mediate between computer code and human comprehension to overcome the limitations of explanations in and for AI systems. Artists can make the boundaries of systems visible and offer novel ways to make the reasoning of AI transparent and decipherable. Beyond this, artistic practice can explore new configurations of humans and algorithms, mapping the terrain of inter-agencies between people and machines. This helps to viscerally understand the complex causal chains in environments with AI components, including questions about what data to collect or who to collect it about, how the algorithms are chosen, commissioned and configured or how humans are conditioned by their participation in algorithmic processes.

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Cited By

Arzberger ALupetti MGiaccardi E(2024)Reflexive Data Curation: Opportunities and Challenges for Embracing Uncertainty in Human–AI CollaborationACM Transactions on Computer-Human Interaction10.1145/368904231:6(1-33)Online publication date: 22-Aug-2024
https://dl.acm.org/doi/10.1145/3689042
Sadek MKallina EBohné TMougenot CCalvo RCave S(2024)Challenges of responsible AI in practice: scoping review and recommended actionsAI & SOCIETY10.1007/s00146-024-01880-9Online publication date: 19-Feb-2024
https://doi.org/10.1007/s00146-024-01880-9
Hemment DCurrie MBennett SElwes JRidler ASinders CVidmar MHill RWarner H(2023)AI in the Public Eye: Investigating Public AI Literacy Through AI ArtProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594052(931-942)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594052
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Information

Published In

cover image AI Matters

AI Matters Volume 5, Issue 1

April 2019

30 pages

EISSN:2372-3483

DOI:10.1145/3320254

Editors:
Amy McGovern
University of Oklahoma
,
Iolanda Leite
KTH

Issue’s Table of Contents

Copyright © 2019 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2019

Published in SIGAI-AIMATTERS Volume 5, Issue 1

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Cited By

Arzberger ALupetti MGiaccardi E(2024)Reflexive Data Curation: Opportunities and Challenges for Embracing Uncertainty in Human–AI CollaborationACM Transactions on Computer-Human Interaction10.1145/368904231:6(1-33)Online publication date: 22-Aug-2024
https://dl.acm.org/doi/10.1145/3689042
Sadek MKallina EBohné TMougenot CCalvo RCave S(2024)Challenges of responsible AI in practice: scoping review and recommended actionsAI & SOCIETY10.1007/s00146-024-01880-9Online publication date: 19-Feb-2024
https://doi.org/10.1007/s00146-024-01880-9
Hemment DCurrie MBennett SElwes JRidler ASinders CVidmar MHill RWarner H(2023)AI in the Public Eye: Investigating Public AI Literacy Through AI ArtProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594052(931-942)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594052
van der Burg Vde Boer GAkdag Salah AChandrasegaran SLloyd P(2023)Objective PortraitProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3595974(387-400)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3563657.3595974
Chicau JReus J(2023)Anatomical Intelligence: Live coding as performative dissectionOrganised Sound10.1017/S1355771823000481(1-15)Online publication date: 22-Aug-2023
https://doi.org/10.1017/S1355771823000481
Yurrita MMurray-Rust DBalayn ABozzon A(2022)Towards a multi-stakeholder value-based assessment framework for algorithmic systemsProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533118(535-563)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533118
Kapur AAnsari N(2022)Coding reality: implications of AI for documentary mediaStudies in Documentary Film10.1080/17503280.2022.204823516:2(174-185)Online publication date: 17-Mar-2022
https://doi.org/10.1080/17503280.2022.2048235
Benjamin JBerger AMerrill NPierce JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Machine Learning Uncertainty as a Design Material: A Post-Phenomenological InquiryProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445481(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445481
Hemment DBelle VAylett RMurray-Rust DPschetz LBroz F(2019)Toward Fairness, Morality and Transparency in Artificial Intelligence through Experiential AILeonardo10.1162/leon_a_0179552:5(426-426)Online publication date: Oct-2019
https://doi.org/10.1162/leon_a_01795

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