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Trustworthy artificial intelligence

Author

Listed:
  • Scott Thiebes

    (Karlsruhe Institute of Technology)

  • Sebastian Lins

    (Karlsruhe Institute of Technology)

  • Ali Sunyaev

    (Karlsruhe Institute of Technology)

Abstract

Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

Suggested Citation

  • Scott Thiebes & Sebastian Lins & Ali Sunyaev, 2021. "Trustworthy artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 447-464, June.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:2:d:10.1007_s12525-020-00441-4
    DOI: 10.1007/s12525-020-00441-4
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    Cited by:

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    4. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    5. Alexandra Brintrup & George Baryannis & Ashutosh Tiwari & Svetan Ratchev & Giovanna Martinez-Arellano & Jatinder Singh, 2023. "Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions," Papers 2305.11581, arXiv.org.
    6. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
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    8. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    9. Aliya Tastemirova & Johannes Schneider & Leona Chandra Kruse & Simon Heinzle & Jan vom Brocke, 2022. "Microexpressions in digital humans: perceived affect, sincerity, and trustworthiness," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1603-1620, September.
    10. Eric K. Clemons & Ravi V. Waran & Sebastian Hermes & Maximilian Schreieck & Helmut Krcmar, 2022. "Computing and Social Welfare," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 417-436, June.
    11. Matti Minkkinen & Markus Philipp Zimmer & Matti Mäntymäki, 2023. "Co-Shaping an Ecosystem for Responsible AI: Five Types of Expectation Work in Response to a Technological Frame," Information Systems Frontiers, Springer, vol. 25(1), pages 103-121, February.
    12. Christian Meske & Babak Abedin & Mathias Klier & Fethi Rabhi, 2022. "Explainable and responsible artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2103-2106, December.
    13. Rodney Duffett & Rodica Milena Zaharia & Tudor Edu & Raluca Constantinescu & Costel Negricea, 2024. "Exploring the Antecedents of Artificial Intelligence Products’ Usage. The Case of Business Students," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 106-106, February.
    14. Md Shajalal & Alexander Boden & Gunnar Stevens, 2022. "Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2107-2122, December.
    15. Rainer Alt, 2021. "Electronic Markets on digital platforms and AI," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 233-241, June.
    16. Rainer Alt, 2022. "Electronic Markets on AI and standardization," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1795-1805, December.
    17. Jana Gerlach & Paul Hoppe & Sarah Jagels & Luisa Licker & Michael H. Breitner, 2022. "Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2139-2158, December.
    18. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    19. René Riedl, 2022. "Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2021-2051, December.
    20. Milad Mirbabaie & Felix Brünker & Nicholas R. J. Möllmann Frick & Stefan Stieglitz, 2022. "The rise of artificial intelligence – understanding the AI identity threat at the workplace," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 73-99, March.
    21. Eufrásio de A. Lima Neto & Jonathan Bailiss & Axel Finke & Jo Miller & Georgina Cosma, 2024. "Identifying early help referrals for local authorities with machine learning and bias analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 385-403, April.
    22. Pascal Hamm & Michael Klesel & Patricia Coberger & H. Felix Wittmann, 2023. "Explanation matters: An experimental study on explainable AI," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-21, December.
    23. Rongbin Yang & Santoso Wibowo, 2022. "User trust in artificial intelligence: A comprehensive conceptual framework," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2053-2077, December.
    24. Wang, Weisha & Wang, Yichuan & Chen, Long & Ma, Rui & Zhang, Minhao, 2024. "Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals," Social Science & Medicine, Elsevier, vol. 347(C).
    25. Roman Lukyanenko & Wolfgang Maass & Veda C. Storey, 2022. "Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1993-2020, December.

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    More about this item

    Keywords

    Trustworthy artificial intelligence; Artificial intelligence; Trust; Framework; Distributed ledger technology; Blockchain;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • A13 - General Economics and Teaching - - General Economics - - - Relation of Economics to Social Values
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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