The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation

• URL: http://arxiv.org/abs/2503.06353v1
• Date: Sat, 08 Mar 2025 22:58:41 GMT
• Title: The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation
• Authors: Di Kevin Gao, Sudip Mittal, Jiming Wu, Hongwei Du, Jingdao Chen, Shahram Rahimi,
• Abstract summary: This paper aims to establish a unifying model for AI regulation from the perspective of core AI components.<n>We first introduce the AI Pentad, which comprises the five essential components of AI.<n>We then review AI regulatory enablers, including AI registration and disclosure, AI monitoring, and AI enforcement mechanisms.
• Score: 5.231576332164012
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespread consensus on the need to regulate AI, there remains a lack of a unified approach to framing, developing, and assessing AI regulations. Many of the existing methods take a value-based approach, for example, accountability, fairness, free from bias, transparency, and trust. However, these methods often face challenges at the outset due to disagreements in academia over the subjective nature of these definitions. This paper aims to establish a unifying model for AI regulation from the perspective of core AI components. We first introduce the AI Pentad, which comprises the five essential components of AI: humans and organizations, algorithms, data, computing, and energy. We then review AI regulatory enablers, including AI registration and disclosure, AI monitoring, and AI enforcement mechanisms. Subsequently, we present the CHARME$^{2}$D Model to explore further the relationship between the AI Pentad and AI regulatory enablers. Finally, we apply the CHARME$^{2}$D model to assess AI regulatory efforts in the European Union (EU), China, the United Arab Emirates (UAE), the United Kingdom (UK), and the United States (US), highlighting their strengths, weaknesses, and gaps. This comparative evaluation offers insights for future legislative work in the AI domain.

Related papers

• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• 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)
• Responsible Artificial Intelligence: A Structured Literature Review [0.0]
  The EU has recently issued several publications emphasizing the necessity of trust in AI. This highlights the urgent need for international regulation. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI.
  arXiv  Detail & Related papers  (2024-03-11T17:01:13Z)
• The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success [4.202570851109354]
  The EU has enacted the AI Act, regulating market access for AI-based systems. The Act focuses regulation on transparency, explainability, and the human ability to understand and control AI systems. The EU issues a democratic call for human-centered AI systems and, in turn, an interdisciplinary research agenda for human-centered innovation in AI development.
  arXiv  Detail & Related papers  (2024-02-22T17:35:29Z)
• How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation [39.58317527488534]
  Recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI.<n>We propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is.
  arXiv  Detail & Related papers  (2024-02-07T17:41:15Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Aligning Artificial Intelligence with Humans through Public Policy [0.0]
  This essay outlines research on AI that learn structures in policy data that can be leveraged for downstream tasks. We believe this represents the "comprehension" phase of AI and policy, but leveraging policy as a key source of human values to align AI requires "understanding" policy.
  arXiv  Detail & Related papers  (2022-06-25T21:31:14Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• 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)
• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [31.382000425295885]
  Technologists and AI researchers have a responsibility to develop trustworthy AI systems. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness.
  arXiv  Detail & Related papers  (2021-01-01T17:34:42Z)

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.