Strategic AI Governance: Insights from Leading Nations
- URL: http://arxiv.org/abs/2410.01819v1
- Date: Mon, 16 Sep 2024 06:00:42 GMT
- Title: Strategic AI Governance: Insights from Leading Nations
- Authors: Dian W. Tjondronegoro,
- Abstract summary: Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities.
This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities. This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations. The key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment. Through a multi-perspective content analysis of the latest AI strategy documents, this paper provides a structured comparison of AI governance strategies across nations. The findings offer valuable insights for governments, academics, industries, and communities to enable responsible and trustworthy AI deployments. Future work should focus on incorporating specific requirements for developing countries and applying the strategies to specific AI applications, industries, and the public sector.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors [1.1060425537315088]
This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms.
We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a startup competition.
We examine implications for key cognitive processes underlying SDM -- search, representation, and aggregation.
arXiv Detail & Related papers (2024-08-16T15:46:15Z) - Open Problems in Technical AI Governance [93.89102632003996]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.
This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - POLARIS: A framework to guide the development of Trustworthy AI systems [3.02243271391691]
There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals.
We develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice.
Our goal is to empower AI professionals to confidently navigate the ethical dimensions of Trustworthy AI.
arXiv Detail & Related papers (2024-02-08T01:05:16Z) - Putting AI Ethics into Practice: The Hourglass Model of Organizational
AI Governance [0.0]
We present an AI governance framework, which targets organizations that develop and use AI systems.
The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice.
arXiv Detail & Related papers (2022-06-01T08:55:27Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - AI Federalism: Shaping AI Policy within States in Germany [0.0]
Recent AI governance research has focused heavily on the analysis of strategy papers and ethics guidelines for AI published by national governments and international bodies.
Subnational institutions have also published documents on Artificial Intelligence, yet these have been largely absent from policy analyses.
This is surprising because AI is connected to many policy areas, such as economic or research policy, where the competences are already distributed between the national and subnational level.
arXiv Detail & Related papers (2021-10-28T16:06:07Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Montreal AI Ethics Institute's Response to Scotland's AI Strategy [0.0]
In January and February 2020, the Scottish Government released two documents for review by the public regarding their artificial intelligence (AI) strategy.
The Montreal AI Ethics Institute (MAIEI) reviewed these documents and published a response on 4 June 2020.
arXiv Detail & Related papers (2020-06-11T10:08:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.