Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare
By Tara Qian Sun, Rony Medaglia. Government Information Quarterly, 2019
726 citations recorded by the source corpus.
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.
Subfields: Political Science and International Relations, Safety Research, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/1913f41dc43f657edc37574583cf50a01940bed8
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Citations in the corpus
- The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, background): “29,40 This issue is further complicated by the absence of industry guidelines on the ethical use of AI and ML in healthcare.”
- Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, background): “A study reports that, in general, individuals are likely to exhibit a lack of trust in the features of AI systems [38].”
- Assessing the Potentials of Digitalization as a Tool for Climate Change Adaptation and Sustainable Development in Urban Centres (2020, background): “…learning and deep learning methods (Norrie, 2018), the adoption of AI in many cities is impeded by concerns related to job losses caused by automation, invasive digital surveillance leading to privacy infringe-ments, and policy-making biases caused by algorithmic governance (Sun & Medaglia, 2018).”