Related papers: Public Constitutional AI

Public Constitutional AI

URL: http://arxiv.org/abs/2406.16696v1
Date: Mon, 24 Jun 2024 15:00:01 GMT
Title: Public Constitutional AI
Authors: Gilad Abiri,
Abstract summary: We are increasingly subjected to the power of AI authorities. How can we ensure AI systems have the legitimacy necessary for effective governance? This essay argues that to secure AI legitimacy, we need methods that engage the public in designing and constraining AI systems.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We are increasingly subjected to the power of AI authorities. As AI decisions become inescapable, entering domains such as healthcare, education, and law, we must confront a vital question: how can we ensure AI systems have the legitimacy necessary for effective governance? This essay argues that to secure AI legitimacy, we need methods that engage the public in designing and constraining AI systems, ensuring these technologies reflect the community's shared values. Constitutional AI, proposed by Anthropic, represents a step towards this goal, offering a model for democratic control of AI. However, while Constitutional AI's commitment to hardcoding explicit principles into AI models enhances transparency and accountability, it falls short in two crucial aspects: addressing the opacity of individual AI decisions and fostering genuine democratic legitimacy. To overcome these limitations, this essay proposes "Public Constitutional AI." This approach envisions a participatory process where diverse stakeholders, including ordinary citizens, deliberate on the principles guiding AI development. The resulting "AI Constitution" would carry the legitimacy of popular authorship, grounding AI governance in the public will. Furthermore, the essay proposes "AI Courts" to develop "AI case law," providing concrete examples for operationalizing constitutional principles in AI training. This evolving combination of constitutional principles and case law aims to make AI governance more responsive to public values. By grounding AI governance in deliberative democratic processes, Public Constitutional AI offers a path to imbue automated authorities with genuine democratic legitimacy, addressing the unique challenges posed by increasingly powerful AI systems while ensuring their alignment with the public interest.

Related papers

Toward Democracy Levels for AI [4.048639768405042]
We provide a "Democracy Levels" framework for evaluating the degree to which decisions in a given domain are made democratically. The framework can be used (i) to define in a roadmap for the democratic AI, pluralistic AI, and public AI ecosystems, (ii) to guide organizations that need to increase the legitimacy of their decisions on difficult AI governance questions, and (iii) as a rubric by those aiming to evaluate AI organizations and keep them accountable.
arXiv Detail & Related papers (2024-11-14T06:37:45Z)
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)
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)
Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z)
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)
The Sanction of Authority: Promoting Public Trust in AI [4.729969944853141]
We argue that public distrust of AI originates from the under-development of a regulatory ecosystem that would guarantee the trustworthiness of the AIs that pervade society. We elaborate the pivotal role of externally auditable AI documentation within this model and the work to be done to ensure it is effective.
arXiv Detail & Related papers (2021-01-22T22:01:30Z)
An Impact Model of AI on the Principles of Justice: Encompassing the Autonomous Levels of AI Legal Reasoning [0.0]
It is argued that the infusion of AI into existing and future legal activities and the judicial structure needs to be undertaken by mindfully observing an alignment with the core principles of justice. By examining the principles of justice across the Levels of Autonomy (LoA) of AI Legal Reasoning, the case is made that there is an ongoing tension underlying the efforts to develop and deploy AI.
arXiv Detail & Related papers (2020-08-26T22:56:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.