Artificial Intelligence and Public Policy

Thierer, Adam D.; O'Sullivan, Andrea; Russell, R. Robert

doi:10.2139/ssrn.3021135

Cited by 47 publications

(10 citation statements)

References 12 publications

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“…Furthermore, participatory mechanisms are formed (Linkov et al, 2018;Sousa et al, 2019a). Public officials and policymakers should analyze and respond to their citizens' demands with exact speed and detail (Thierer et al, 2017). Otherwise, the problems cannot be solved quickly, and crises arise.…”

Section: Question 1 How Do Ai-driven Ecosystems Transform the Art Of ...mentioning

confidence: 99%

“…The active use of AI in the public policy cycle can supply a suitable opportunity to understand the complexity of public decision-making processes and the actors involved. Furthermore, AI can exert a data-driven empirical impact on the public policy cycle (Thierer et al, 2017). In addition, using AI can drive e-government instruments to be more practical and functional.…”

Section: Question 1 How Do Ai-driven Ecosystems Transform the Art Of ...mentioning

confidence: 99%

“…AI governance is also defined as "tackling the challenges and risk posed by AI" (Wirtz et al, 2020). On the other hand, some scholars express the concept of AI governance as AI regulation (Almeida et al, 2020;Gasser & Almeida, 2017;Thierer et al, 2017;Wang & Siau, 2018). For example, Gasser and Almeida (2017, p. 59) pointed out, "When considering future governance models for AI, it might be helpful and necessary to move beyond such lists and consider some of the larger structural challenges associated with the 'regulation' of AI-based technologies."…”

Section: Question 2 How Should Ai Governance and Regulations Be Formed?mentioning

confidence: 99%

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Big Questions of Artificial Intelligence (AI) in Public Administration and Policy

Uzun¹,

Yıldız²,

Önder³

2022

jps

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Technological advancements have created notable turning points throughout the history of humanity. Influential transformations in the administrative structures are the result of modern technological discoveries. The artificial intelligence (AI) ecosystems and algorithms now affect daily lives, communities, and government structures more than ever. Governments are the main coordinators of technological transition and supervisors of the activities of modern public administration systems. Hence, public administration and policies have crucial responsibilities in integrating, governing, and regulating AI technology. Integrating AI into public administration and the policy-making process allows numerous opportunities. However, AI technology also contains many threats and risks in economic, social, and even political structures in the long term. This article concentrates on the big questions of AI in the public administration and policy literature. The big questions discussion started in 1995 by Robert Behn drawing attention to the big questions as the primary driving force of a public administration research agenda. The fundamental motivation of the big questions approach is shaped by the fact that "questions are as important as answers." This article aims to identify big questions and discuss potential answers and solutions from an AI governance research agenda perspective.

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Section: Question 1 How Do Ai-driven Ecosystems Transform the Art Of ...mentioning

confidence: 99%

Section: Question 1 How Do Ai-driven Ecosystems Transform the Art Of ...mentioning

confidence: 99%

Section: Question 2 How Should Ai Governance and Regulations Be Formed?mentioning

confidence: 99%

See 1 more Smart Citation

Big Questions of Artificial Intelligence (AI) in Public Administration and Policy

Uzun¹,

Yıldız²,

Önder³

2022

jps

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“…The high acuity of spinal disorders and the ramifications of a misdiagnosis within spinal surgery make this especially relevent. 45 , 46 As diagnostic ML tools achieve better performance within spine surgery and clinicians come to more heavily rely on automated diagnoses this ethical issue will need to be debated further.…”

Section: Future Directionsmentioning

confidence: 99%

Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

Merali

Colak

Wilson

2021

Global Spine Journal

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Study Design: Narrative review. Objectives: We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. Methods: A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. Results: Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. Conclusion: Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.

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“…There is no doubt of the beneficial effects of data science on social life enhancement in fields such as pinpointing fraudulent actions [102,103] helping in medicine diagnostics [104], training of professionals via simulation [105], or aiding mobility through autonomous systems [106]. However, some concerns are also present [107]. Specifically, there are mentions of results without theory, like the infamous Garbage In, Garbage Out (GIGO) [108].…”

Section: Data Miningmentioning

confidence: 99%

Policy Modeling and Applications: State‐of‐the‐Art and Perspectives

2019

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The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent-based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent-based modeling has established itself as a strong tool within scientific realm. However, adoption by policy-makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.

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Artificial Intelligence and Public Policy

Cited by 47 publications

References 12 publications

Big Questions of Artificial Intelligence (AI) in Public Administration and Policy

Big Questions of Artificial Intelligence (AI) in Public Administration and Policy

Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

Policy Modeling and Applications: State‐of‐the‐Art and Perspectives

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