Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness

• URL: http://arxiv.org/abs/2601.06223v1
• Date: Fri, 09 Jan 2026 07:27:43 GMT
• Title: Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness
• Authors: Edward C. Cheng, Jeshua Cheng, Alice Siu,
• Abstract summary: This paper presents a conceptual and operational framework for developing and operating safe and trustworthy AI agents.<n>The framework is based on a Three-Pillar Model grounded in transparency, accountability, and trustworthiness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a conceptual and operational framework for developing and operating safe and trustworthy AI agents based on a Three-Pillar Model grounded in transparency, accountability, and trustworthiness. Building on prior work in Human-in-the-Loop systems, reinforcement learning, and collaborative AI, the framework defines an evolutionary path toward autonomous agents that balances increasing automation with appropriate human oversight. The paper argues that safe agent autonomy must be achieved through progressive validation, analogous to the staged development of autonomous driving, rather than through immediate full automation. Transparency and accountability are identified as foundational requirements for establishing user trust and for mitigating known risks in generative AI systems, including hallucinations, data bias, and goal misalignment, such as the inversion problem. The paper further describes three ongoing work streams supporting this framework: public deliberation on AI agents conducted by the Stanford Deliberative Democracy Lab, cross-industry collaboration through the Safe AI Agent Consortium, and the development of open tooling for an agent operating environment aligned with the Three-Pillar Model. Together, these contributions provide both conceptual clarity and practical guidance for enabling the responsible evolution of AI agents that operate transparently, remain aligned with human values, and sustain societal trust.

Related papers

• Responsible AI in Business [0.8213113085481418]
  It structures Responsible AI along four focal areas that are central for introducing and operating AI systems in a legally compliant, comprehensible, sustainable, and data-sovereign manner.<n>First, it discusses the EU AI Act as a risk-based regulatory framework, including the distinction between provider and deployer roles.<n>Second, it addresses Explainable AI as a basis for transparency and trust, clarifying key notions such as transparency, interpretability, and explainability.<n>Third, it covers Green AI, emphasizing that AI systems should be evaluated not only by performance but also by energy and resource consumption.
  arXiv  Detail & Related papers  (2026-01-31T08:24:20Z)
• Institutional AI: A Governance Framework for Distributional AGI Safety [1.3763052684269788]
  We identify three structural problems that emerge from core properties of AI models.<n>The solution is Institutional AI, a system-level approach that treats alignment as a question of effective governance of AI agent collectives.
  arXiv  Detail & Related papers  (2026-01-15T17:08:26Z)
• Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning [4.226647687395254]
  This paper presents a Responsible(RAI) and Explainable(XAI) AI Agent Architecture for production-grade agentic based on multi-model consensus and reasoning-layer governance.<n>In the proposed design, a consortium of heterogeneous LLM and VLM agents independently generates candidate outputs from a shared input context.<n>A dedicated reasoning agent then performs structured consolidation across these outputs, enforcing safety and policy constraints, mitigating hallucinations and bias, and producing auditable, evidence-backed decisions.
  arXiv  Detail & Related papers  (2025-12-25T14:49:25Z)
• DoubleAgents: Exploring Mechanisms of Building Trust with Proactive AI [29.777890680647186]
  DoubleAgents is an agentic planning tool that embeds transparency and control through user intervention.<n>A built-in respondent simulation generates realistic scenarios, allowing users to rehearse, refine policies, and calibrate their reliance.
  arXiv  Detail & Related papers  (2025-09-16T03:43:13Z)
• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges [49.69200207497795]
  The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems.<n>This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms.
  arXiv  Detail & Related papers  (2025-08-04T15:44:58Z)
• LLM Agents Should Employ Security Principles [60.03651084139836]
  This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
  arXiv  Detail & Related papers  (2025-05-29T21:39:08Z)
• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios.<n>Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes [0.7270112855088837]
  With the rise of generative AI, industry interest in software agents is growing.<n>This paper investigates how organizations can effectively govern AI agents.<n>It outlines six key recommendations for the responsible adoption of AI agents.
  arXiv  Detail & Related papers  (2025-03-23T20:15:24Z)
• Can We Govern the Agent-to-Agent Economy? [0.0]
  Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks.<n>We highlight emerging concepts in the industry to inform research and development efforts in anticipation of a future decentralized agentic economy.
  arXiv  Detail & Related papers  (2025-01-28T00:50:35Z)
• Decentralized Governance of Autonomous AI Agents [0.0]
  ETHOS is a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs)<n>It establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring.<n>By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance.
  arXiv  Detail & Related papers  (2024-12-22T18:01:49Z)
• 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)
• Designing for Responsible Trust in AI Systems: A Communication Perspective [56.80107647520364]
  We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH. We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. We propose a checklist of requirements to help technology creators identify appropriate cues to use.
  arXiv  Detail & Related papers  (2022-04-29T00:14:33Z)

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.