AI in Government

Permanent URI for this collectionhttps://hdl.handle.net/10125/107463

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    Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act
    (2024-01-03) Koh, Fiona; Grosse, Kathrin; Apruzzese, Giovanni
    Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the European Commission is working on the AI Act—a regulation designed to ensure a trustworthy development of AI that is respectful of its end-users' well-being. Despite the impact this law will have on the AI industry, few studies cover more than two or three aspects of the AI Act from the industry's perspective. In this paper, we attempt to close this gap and interview 21 companies to understand their holistic view on the upcoming regulatory landscape. We find that while the overall opinion on the AI Act is positive, there is a need for further resources like personnel and information to increase legitimacy. We further shed light on our companies' desiderata for the AI Act: more fine-grained regulation, and more AI expert input. Lastly, we identify avenues for future research, entailing machine unlearning and a deeper understanding of industry's perception on current legislation.
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    Public managers perception on artificial intelligence: the case of the State of Mexico
    (2024-01-03) Millan Vargas, Adrian; Sandoval-Almazán, Rodrigo
    The use and implementation of Artificial Intelligence (AI) tools for doing repetitive tasks in the public sector is a challenge, particularly in persuading bureaucrats. However, the potential benefits for citizens, such as improved process and services related to tax payments and basic services using machine learning or diffuse logic for decision making or logistic distribution, are significant. This research aims to investigate public managers' competencies to face AI challenges in the public sector. A survey was conducted among 32 key public managers from the government of the State of Mexico in the central region to assess their perceptions of AI. The findings indicate that there is a lack of skills and a limited understanding of AI among public managers, potentially hindering its future implementation. This study is relevant as it identifies the level of competency development for the implementation of AI in a local government.
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    Introduction to the Minitrack on AI in Government
    (2024-01-03) Carter, Lemuria; Gasco-Hernandez, Mila; Liu, Dapeng
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    National AI Strategic Plans for the Public versus Private Sectors: A Cross-Cultural Configurational Analysis
    (2024-01-03) Denford, James; Dawson, Gregory S.; Desouza, Kevin
    We use a fuzzy set Qualitative Comparative Analysis (fsQCA) approach to analyze the national Artificial Intelligence (AI) strategic plans of 34 countries. Applying Hofstede's four-dimension cultural model, we find that countries develop their national AI strategic plans around public and private sector policies in a manner that is consistent with their national cultures and, if they only place emphasis on one, it will generally be on industry. We also find that the most critical differentiators between detailed versus limited plan development are task/people orientation and individualism/collectivism, where high collectivism and high task orientation are linked to more detailed national AI plans and policies.
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    A Typology for AI-enhanced Online Ideation: Application to Digital Participation Platforms
    (2024-01-03) Bono Rossello, Nicolas; Simonofski, Anthony; Clarinval, Antoine; Castiaux, Annick
    Digital Participation Platforms (DPP) can enable a massive online participation of citizens in policy-making. However, this digital channel brings new challenges for citizens in the form of information overload and asynchronous dialogues. Various disciplines of online ideation provide different AI-based approaches to tackle these challenges, but the literature remains fragmented. In consequence, this paper develops a typology for online ideation consisting of six types of AI-enhanced solutions. The application of this typology to DPP shows a prominence of automated tasks, with few AI-human loop approaches, and a current lack of applications at the collective level. This general typology also allows us to compare current DPP solutions to other fields, such as open innovation or recommender systems, and to use these fields as inspiration for future solutions. Overall, this paper suggests a theoretical foundation to analyze AI-enhanced online ideation under the form of a typology. Its application to DPP enables identifying future research opportunities and serves as a basis to develop complex architectures for the use of AI in DPP.