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Improving public services using artificial intelligence: possibilities, pitfalls, governance

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  • Paul Henman

Abstract

Artificial intelligence arising from the use of machine learning is rapidly being developed and deployed by governments to enhance operations, public services, and compliance and security activities. This article reviews how artificial intelligence is being used in public sector for automated decision making, for chatbots to provide information and advice, and for public safety and security. It then outlines four public administration challenges to deploying artificial intelligence in public administration: accuracy, bias and discrimination; legality, due process and administrative justice; responsibility, accountability, transparency and explainability; and power, compliance and control. The article outlines technological and governance innovations that are being developed to address these challenges.

Suggested Citation

  • Paul Henman, 2020. "Improving public services using artificial intelligence: possibilities, pitfalls, governance," Asia Pacific Journal of Public Administration, Taylor & Francis Journals, vol. 42(4), pages 209-221, October.
    • Handle: RePEc:taf:rapaxx:v:42:y:2020:i:4:p:209-221
    DOI: 10.1080/23276665.2020.1816188
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    Cited by:

    1. Jonathan Jacob Paul Latupeirissa & Ni Luh Yulyana Dewi & I Kadek Rian Prayana & Melati Budi Srikandi & Sahri Aflah Ramadiansyah & Ida Bagus Gde Agung Yoga Pramana, 2024. "Transforming Public Service Delivery: A Comprehensive Review of Digitization Initiatives," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
    2. Pedro R. Palos-Sánchez & Pedro Baena-Luna & Mercedes García-Ordaz & Francisco J. Martínez-López, 2023. "Digital Transformation and Local Government Response to the COVID-19 Pandemic: An Assessment of Its Impact on the Sustainable Development Goals," SAGE Open, , vol. 13(2), pages 21582440231, April.
    3. Yu-Che Chen & Michael J. Ahn & Yi-Fan Wang, 2023. "Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    4. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    5. Ha, Le Thanh, 2023. "An investigation of digital integration's importance on smart and sustainable agriculture in the European region," Resources Policy, Elsevier, vol. 86(PA).
    6. Mirwais Ahmadzai & Giang Nguyen, 2024. "Differential Private Federated Learning in Geographically Distributed Public Administration Processes," Future Internet, MDPI, vol. 16(7), pages 1-20, June.
    7. Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022. "Differentiating artificial intelligence activity clusters in Australia," Technology in Society, Elsevier, vol. 71(C).
    8. Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022. "Differentiating artificial intelligence capability clusters in Australia," MPRA Paper 113237, University Library of Munich, Germany.
    9. Kumar, Shashank & Raut, Rakesh D. & Queiroz, Maciel M. & Narkhede, Balkrishna E., 2021. "Mapping the barriers of AI implementations in the public distribution system: The Indian experience," Technology in Society, Elsevier, vol. 67(C).
    10. Silvia Rolandi & Gianluca Brunori & Manlio Bacco & Ivano Scotti, 2021. "The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts," Sustainability, MDPI, vol. 13(9), pages 1-16, May.

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