Mapping the Challenges of Artificial Intelligence in the Public Sector: Evidence from Public Healthcare

Qian Sun, Rony Medaglia

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The nascent adoption of Artificial Intelligence (AI) in the public sector is being assessed in contradictory ways. But while there is increasing speculation about both its dangers and its benefits, there is very little empirical research to substantiate them. This study aims at mapping the challenges in the adoption of AI in the public sector as perceived by key stakeholders. Drawing on the theoretical lens of framing, we analyse a case of adoption of the AI system IBM Watson in public healthcare in China, to map how three groups of stakeholders (government policy-makers, hospital managers/doctors, and Information Technology (IT) firm managers) perceive the challenges of AI adoption in the public sector. Findings show that different stakeholders have diverse, and sometimes contradictory, framings of the challenges. We contribute to research by providing an empirical basis to claims of AI challenges in the public sector, and to practice by providing four sets of guidelines for the governance of AI adoption in the public sector.
The nascent adoption of Artificial Intelligence (AI) in the public sector is being assessed in contradictory ways. But while there is increasing speculation about both its dangers and its benefits, there is very little empirical research to substantiate them. This study aims at mapping the challenges in the adoption of AI in the public sector as perceived by key stakeholders. Drawing on the theoretical lens of framing, we analyse a case of adoption of the AI system IBM Watson in public healthcare in China, to map how three groups of stakeholders (government policy-makers, hospital managers/doctors, and Information Technology (IT) firm managers) perceive the challenges of AI adoption in the public sector. Findings show that different stakeholders have diverse, and sometimes contradictory, framings of the challenges. We contribute to research by providing an empirical basis to claims of AI challenges in the public sector, and to practice by providing four sets of guidelines for the governance of AI adoption in the public sector.
LanguageEnglish
JournalGovernment Information Quarterly
Volume36
Issue number2
Pages368-383
Number of pages16
ISSN0740-624X
DOIs
StatePublished - 2019

Bibliographical note

Published online: 9. October 2018

Keywords

  • Artificial intelligence
  • Public sector
  • Healthcare
  • Challenges
  • Framing
  • China

Cite this

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Mapping the Challenges of Artificial Intelligence in the Public Sector : Evidence from Public Healthcare. / Sun, Qian; Medaglia, Rony.

In: Government Information Quarterly, Vol. 36, No. 2, 2019, p. 368-383.

Research output: Contribution to journalJournal articleResearchpeer-review

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