Toward a Public and Secure Generative AI: A Comparative Analysis of Open and Closed LLMs
- URL: http://arxiv.org/abs/2505.10603v1
- Date: Thu, 15 May 2025 15:21:09 GMT
- Title: Toward a Public and Secure Generative AI: A Comparative Analysis of Open and Closed LLMs
- Authors: Jorge Machado,
- Abstract summary: This study aims to critically evaluate and compare the characteristics, opportunities, and challenges of open and closed generative AI models.<n>The proposed framework outlines key dimensions, openness, public governance, and security, as essential pillars for shaping the future of trustworthy and inclusive Gen AI.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Generative artificial intelligence (Gen AI) systems represent a critical technology with far-reaching implications across multiple domains of society. However, their deployment entails a range of risks and challenges that require careful evaluation. To date, there has been a lack of comprehensive, interdisciplinary studies offering a systematic comparison between open-source and proprietary (closed) generative AI systems, particularly regarding their respective advantages and drawbacks. This study aims to: i) critically evaluate and compare the characteristics, opportunities, and challenges of open and closed generative AI models; and ii) propose foundational elements for the development of an Open, Public, and Safe Gen AI framework. As a methodology, we adopted a combined approach that integrates three methods: literature review, critical analysis, and comparative analysis. The proposed framework outlines key dimensions, openness, public governance, and security, as essential pillars for shaping the future of trustworthy and inclusive Gen AI. Our findings reveal that open models offer greater transparency, auditability, and flexibility, enabling independent scrutiny and bias mitigation. In contrast, closed systems often provide better technical support and ease of implementation, but at the cost of unequal access, accountability, and ethical oversight. The research also highlights the importance of multi-stakeholder governance, environmental sustainability, and regulatory frameworks in ensuring responsible development.
Related papers
- Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems [0.0]
This paper introduces an assessment method that combines the ethical components of Trustworthy AI with the algorithmic processes of PageRank and TrustRank.<n>The goal is to establish an assessment framework that minimizes the subjectivity inherent in the self-assessment techniques prevalent in the field.
arXiv Detail & Related papers (2025-06-28T06:27:30Z) - Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China [0.0]
This paper conducts a comparative analysis of AI risk management strategies across the European Union, United States, United Kingdom (UK), and China.<n>The findings show that the EU implements a structured, risk-based framework that prioritizes transparency and conformity assessments.<n>The U.S. uses a decentralized, sector-specific regulations that promote innovation but may lead to fragmented enforcement.
arXiv Detail & Related papers (2025-02-25T18:52:17Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)<n>RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks.<n>Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
arXiv Detail & Related papers (2025-02-08T06:50:47Z) - Safety is Essential for Responsible Open-Ended Systems [47.172735322186]
Open-Endedness is the ability of AI systems to continuously and autonomously generate novel and diverse artifacts or solutions.<n>This position paper argues that the inherently dynamic and self-propagating nature of Open-Ended AI introduces significant, underexplored risks.
arXiv Detail & Related papers (2025-02-06T21:32:07Z) - Democratizing AI Governance: Balancing Expertise and Public Participation [1.0878040851638]
The development and deployment of artificial intelligence (AI) systems, with their profound societal impacts, raise critical challenges for governance.<n>This article explores the tension between expert-led oversight and democratic participation, analyzing models of participatory and deliberative democracy.<n> Recommendations are provided for integrating these approaches into a balanced governance model tailored to the European Union.
arXiv Detail & Related papers (2025-01-16T17:47:33Z) - A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications [2.0681376988193843]
"Black box" characteristic of AI models constrains interpretability, transparency, and reliability.<n>This study presents a unified XAI evaluation framework to evaluate correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models.
arXiv Detail & Related papers (2024-12-05T05:30:10Z) - Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation.
This regulation is likely to put at risk the budding field of open-source generative AI.
arXiv Detail & Related papers (2024-05-14T13:37:36Z) - Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER)
SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
arXiv Detail & Related papers (2024-04-07T07:05:59Z) - Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance [14.941040909919327]
Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact.
Recent studies have identified new attack surfaces and risks caused by security, privacy, and fairness issues in AI systems.
We review representative techniques, algorithms, and theoretical foundations for trustworthy distributed AI.
arXiv Detail & Related papers (2024-02-02T01:58:58Z) - 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) - Trustworthy AI [75.99046162669997]
Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations.
We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
arXiv Detail & Related papers (2020-11-02T20:04:18Z)
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.