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Improving trust in data and algorithms in the medium of AI

Author

Listed:
  • Aditya Vasan Srinivasan

    (TU Delft, Amsterdam, Netherlands)

  • Mona de Boer

    (PwC, Amsterdam, Netherlands)

Abstract

Artificial Intelligence (AI) has great potential to solve a wide spectrum of real-world business problems, but the lack of trust from the perspective of potential users, investors, and other stakeholders towards AI is preventing them from adoption. To build and strengthen trust in AI, technology creators should ensure that the data which is acquired, processed and being fed into the algorithm is accurate, reliable, consistent, relevant, bias-free, and complete. Similarly, the algorithm that is selected, trained, and tested should be explainable, interpretable, transparent, bias-free, reliable, and useful. Most importantly, the algorithm and its outcomes should be auditable and properly governed.

Suggested Citation

  • Aditya Vasan Srinivasan & Mona de Boer, 2020. "Improving trust in data and algorithms in the medium of AI," Maandblad Voor Accountancy en Bedrijfseconomie Articles, Maandblad Voor Accountancy en Bedrijfseconomie, vol. 94(3-4), pages 147-160, April.
    • Handle: RePEc:arh:jmabec:v:94:y:2020:i:3-4:p:147-160
    DOI: 10.5117/mab.94.49425
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    Cited by:

    1. Sudatta Kar & Arpan Kumar Kar & Manmohan Prasad Gupta, 2021. "Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 217-238, October.

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