Establishing and evaluating trustworthy AI: overview and research challenges
Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1467222/full |
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| author | Dominik Kowald Dominik Kowald Sebastian Scher Sebastian Scher Viktoria Pammer-Schindler Viktoria Pammer-Schindler Peter Müllner Kerstin Waxnegger Lea Demelius Lea Demelius Angela Fessl Angela Fessl Maximilian Toller Inti Gabriel Mendoza Estrada Ilija Šimić Vedran Sabol Andreas Trügler Andreas Trügler Andreas Trügler Eduardo Veas Eduardo Veas Roman Kern Roman Kern Tomislav Nad Simone Kopeinik |
| author_facet | Dominik Kowald Dominik Kowald Sebastian Scher Sebastian Scher Viktoria Pammer-Schindler Viktoria Pammer-Schindler Peter Müllner Kerstin Waxnegger Lea Demelius Lea Demelius Angela Fessl Angela Fessl Maximilian Toller Inti Gabriel Mendoza Estrada Ilija Šimić Vedran Sabol Andreas Trügler Andreas Trügler Andreas Trügler Eduardo Veas Eduardo Veas Roman Kern Roman Kern Tomislav Nad Simone Kopeinik |
| author_sort | Dominik Kowald |
| collection | DOAJ |
| description | Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: (1) human agency and oversight, (2) fairness and non-discrimination, (3) transparency and explainability, (4) robustness and accuracy, (5) privacy and security, and (6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to (1) interdisciplinary research, (2) conceptual clarity, (3) context-dependency, (4) dynamics in evolving systems, and (5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions. |
| format | Article |
| id | doaj-art-67a4365c88244a38aee89c7ec1c5aa5e |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-67a4365c88244a38aee89c7ec1c5aa5e2024-11-29T07:14:32ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2024-11-01710.3389/fdata.2024.14672221467222Establishing and evaluating trustworthy AI: overview and research challengesDominik Kowald0Dominik Kowald1Sebastian Scher2Sebastian Scher3Viktoria Pammer-Schindler4Viktoria Pammer-Schindler5Peter Müllner6Kerstin Waxnegger7Lea Demelius8Lea Demelius9Angela Fessl10Angela Fessl11Maximilian Toller12Inti Gabriel Mendoza Estrada13Ilija Šimić14Vedran Sabol15Andreas Trügler16Andreas Trügler17Andreas Trügler18Eduardo Veas19Eduardo Veas20Roman Kern21Roman Kern22Tomislav Nad23Simone Kopeinik24Know Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaKnow Center Research GmbH, Graz, AustriaDepartment of Geography and Regional Science, Wegener Center for Climate and Global Change, University of Graz, Graz, AustriaKnow Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaKnow Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaDepartment of Geography and Regional Science, Wegener Center for Climate and Global Change, University of Graz, Graz, AustriaKnow Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaKnow Center Research GmbH, Graz, AustriaInstitute of Interactive Systems and Data Science, Graz University of Technology, Graz, AustriaSGS Digital Trusts Services GmbH, Graz, AustriaKnow Center Research GmbH, Graz, AustriaArtificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: (1) human agency and oversight, (2) fairness and non-discrimination, (3) transparency and explainability, (4) robustness and accuracy, (5) privacy and security, and (6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to (1) interdisciplinary research, (2) conceptual clarity, (3) context-dependency, (4) dynamics in evolving systems, and (5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions.https://www.frontiersin.org/articles/10.3389/fdata.2024.1467222/fulltrustworthy AIartificial intelligencefairnesshuman agencyrobustnessprivacy |
| spellingShingle | Dominik Kowald Dominik Kowald Sebastian Scher Sebastian Scher Viktoria Pammer-Schindler Viktoria Pammer-Schindler Peter Müllner Kerstin Waxnegger Lea Demelius Lea Demelius Angela Fessl Angela Fessl Maximilian Toller Inti Gabriel Mendoza Estrada Ilija Šimić Vedran Sabol Andreas Trügler Andreas Trügler Andreas Trügler Eduardo Veas Eduardo Veas Roman Kern Roman Kern Tomislav Nad Simone Kopeinik Establishing and evaluating trustworthy AI: overview and research challenges Frontiers in Big Data trustworthy AI artificial intelligence fairness human agency robustness privacy |
| title | Establishing and evaluating trustworthy AI: overview and research challenges |
| title_full | Establishing and evaluating trustworthy AI: overview and research challenges |
| title_fullStr | Establishing and evaluating trustworthy AI: overview and research challenges |
| title_full_unstemmed | Establishing and evaluating trustworthy AI: overview and research challenges |
| title_short | Establishing and evaluating trustworthy AI: overview and research challenges |
| title_sort | establishing and evaluating trustworthy ai overview and research challenges |
| topic | trustworthy AI artificial intelligence fairness human agency robustness privacy |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1467222/full |
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