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An Assessment of the Barriers Impacting Responsible Artificial Intelligence

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  • Mohammad I. Merhi

    (Indiana University South Bend)

Abstract

Responsible Artificial Intelligence (AI) has recently gained a lot of attention, especially in the last few years. Scholars have conducted systematic literature reviews to gain more knowledge about responsible AI. However, no study has collected and evaluated the most significant barriers to responsible AI. We filled this gap in the literature by identifying eleven barriers and categorized them, using the Technology-Organization-Environment framework, into three categories. We collected data from seven experts and used the analytical hierarchy process to evaluate the importance of the barriers. The results indicated that technology, as a category, is the most important. The findings also recommended that data quality is the most vital among all eleven barriers. We offered eleven propositions as a theoretical contribution for future researchers in terms of conceptual development. We discussed the implications of the findings for research and practice.

Suggested Citation

  • Mohammad I. Merhi, 2023. "An Assessment of the Barriers Impacting Responsible Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(3), pages 1147-1160, June.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:3:d:10.1007_s10796-022-10276-3
    DOI: 10.1007/s10796-022-10276-3
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    References listed on IDEAS

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    Cited by:

    1. Antoine Harfouche & Mohammad I. Merhi & Abdullah Albizri & Denis Dennehy & Jason Bennett Thatcher, 2024. "Sustainable Development Through Technological Innovations and Data Analytics," Information Systems Frontiers, Springer, vol. 26(6), pages 1989-1996, December.
    2. Daniel Leuthe & Tim Meyer-Hollatz & Tobias Plank & Anja Senkmüller, 2024. "Towards Sustainability of AI – Identifying Design Patterns for Sustainable Machine Learning Development," Information Systems Frontiers, Springer, vol. 26(6), pages 2103-2145, December.
    3. Singh, Nidhi & Jain, Monika & Kamal, Muhammad Mustafa & Bodhi, Rahul & Gupta, Bhumika, 2024. "Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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