Add scraped data for 2505.01651

Author: miklosme

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+{
+  "arxivId": "2505.01651",
+  "title": "Human-AI Governance (HAIG): A Trust-Utility Approach",
+  "abstract": "This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., \"human-in-the-loop\" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rather than discrete categories, though progression may include both gradual shifts and significant step changes. The HAIG framework operates across three levels: dimensions (Decision Authority Distribution, Process Autonomy, and Accountability Configuration), continua (gradual shifts along each dimension), and thresholds (critical points requiring governance adaptation). Unlike risk-based or principle-based approaches, HAIG adopts a trust-utility orientation, focusing on maintaining appropriate trust relationships that maximise utility while ensuring sufficient safeguards. Our analysis reveals how technical advances in self-supervision, reasoning authority, and distributed decision-making drive non-uniform trust evolution across both contextual variation and technological advancement. Case studies in healthcare and European regulation demonstrate how HAIG complements existing frameworks while offering a foundation for alternative approaches that anticipate governance challenges before they emerge.",
+  "summary": "This paper introduces the Human-AI Governance (HAIG) framework, a new way of thinking about how we manage and regulate AI as it becomes more sophisticated, especially in multi-agent systems. Instead of sorting AI systems into fixed categories, HAIG looks at how human-AI relationships change gradually along three scales: who has decision-making power, how much the AI can operate on its own, and who is responsible when things go wrong.\n\nKey points for LLM-based multi-agent systems include:  Emergent capabilities of LLMs require flexible governance that adapts to changing AI behavior instead of relying on fixed rules. The ability of LLMs to reason and explain their decisions becomes crucial for establishing trust, especially as they take on more decision-making authority.  Context matters – the same LLM can require different governance approaches depending on how it's used. As multi-agent systems become more common, we need to shift from trusting individual AIs to trusting how they interact as a group.  \"Trust dynamics,\" or how trust is built and maintained, should be central to how we govern AI.  HAIG emphasizes making AI useful while minimizing harm, with stronger oversight for AI systems with greater authority.",
+  "takeaways": "This paper provides a robust framework (HAIG) for navigating the evolving landscape of human-AI interaction, particularly relevant for JavaScript developers working with LLM-based multi-agent systems.  Here's how a JavaScript developer can apply these insights:\n\n**1. Decision Authority Distribution:**\n\n* **Scenario:** Building a collaborative writing tool using LLMs where multiple users and AI agents contribute to a document.\n* **HAIG Insight:** Decision authority can shift dynamically.  Initially, the user might have full control. As AI capabilities improve, authority can be shared, with the AI suggesting text, completing sentences, or even drafting entire paragraphs. Eventually, the AI could assume more authority in tasks like grammar correction, stylistic adjustments, or summarization.\n* **JavaScript Implementation:**\n    * Use a framework like React or Vue to manage the UI and state.\n    * Implement different interaction modes representing varying levels of AI authority. For example, a toggle switch could allow users to choose between \"AI Assist,\" \"AI Suggest,\" and \"AI Draft.\"\n    * Leverage libraries like `socket.io` for real-time collaboration between users and AI agents.  Track changes and allow users to accept or reject AI suggestions.\n    * Create a \"confidence score\" display for AI-generated content using a visualization library like D3.js, allowing users to assess the reliability of AI contributions.\n\n**2. Process Autonomy:**\n\n* **Scenario:** Creating a multi-agent e-commerce chatbot system where AI agents handle customer inquiries, order processing, and inventory management.\n* **HAIG Insight:**  Process autonomy increases as AI handles more complex tasks without human intervention.  This requires robust monitoring and well-defined boundaries.\n* **JavaScript Implementation:**\n    * Use Node.js with a framework like Express.js to build the backend for the chatbot system.\n    * Implement AI agents as separate modules, potentially using a message queue like RabbitMQ for communication.\n    * Integrate logging and monitoring tools to track agent activities. Libraries like Winston or Pino can be used for logging, and tools like Prometheus or Grafana for monitoring.\n    * Define clear exception handling routines for scenarios where AI agents encounter issues or reach the limits of their capabilities. For instance, if an agent cannot understand a customer's request, the system should escalate to a human operator. This could be done by emitting an event that triggers a notification in a dedicated admin interface built with React.\n\n**3. Accountability Configuration:**\n\n* **Scenario:** Developing a multi-agent system for content moderation on a social media platform.\n* **HAIG Insight:** As AI agents assume more authority in moderation decisions, robust accountability mechanisms become crucial.\n* **JavaScript Implementation:**\n    * Store a detailed audit trail of every moderation decision, including the agent involved, the content evaluated, the decision made, and the rationale behind it. Use a NoSQL database like MongoDB for flexible storage.\n    * Implement explainability features using techniques like LIME or SHAP to provide insights into the reasoning behind moderation decisions. Use a visualization library to display these explanations to human moderators.\n    * Design appeal mechanisms that allow users to contest moderation decisions. The appeal process should involve human review and provide clear explanations of the final decision.\n    * Build dashboards using a visualization library to track moderation performance metrics like accuracy, false positive rate, and appeal success rate. This helps identify potential biases or areas for improvement.\n\n**4. Trust Thresholds:**\n\n* **Scenario:**  A web app uses an LLM to generate personalized learning recommendations for students.\n* **HAIG Insight:** Initially, the system might simply provide information about different learning resources. As the AI learns more about the student's preferences and learning style, it could cross the \"Information to Recommendation\" threshold and start suggesting specific courses or learning paths. This requires careful trust calibration.\n* **JavaScript Implementation:**\n    * Implement A/B testing to compare different levels of AI authority in recommendations.  Track user engagement metrics to determine the optimal balance between AI suggestions and user autonomy.\n    * Allow users to provide feedback on the AI's recommendations. This feedback can be used to refine the AI model and improve its accuracy.\n    * Gradually increase the AI's role in recommendations as trust is established, potentially using a phased rollout strategy. Start with simple recommendations and progressively increase their complexity and personalization as users become more comfortable with the system.\n\nBy incorporating these HAIG principles, JavaScript developers can build more robust, transparent, and accountable LLM-based multi-agent systems. These examples highlight how abstract concepts from the paper translate into tangible code considerations, paving the way for responsible and user-centric AI development. They also show how JavaScript's rich ecosystem of libraries and frameworks provides the necessary tools for practically implementing these complex AI governance principles within web applications.  This is particularly crucial as multi-agent AI becomes increasingly prevalent in shaping user experiences and impacting societal outcomes.",
+  "pseudocode": "No pseudocode block found.",
+  "simpleQuestion": "How can I build trustworthy, useful multi-agent AI?",
+  "timestamp": "2025-05-06T05:05:51.370Z"
+}