Usage Governance Advisor: from Intent to AI Governance

2024-12-02

arXiv.org

AI Summary

To address governance challenges concerning safety, fairness, compliance, and privacy preservation in AI system deployment, this paper proposes the first intent-driven, end-to-end AI risk governance framework. The method automatically constructs semi-structured governance knowledge by parsing user intents and integrating heterogeneous, multi-source information; it employs knowledge graph modeling alongside rule- and model-based collaborative reasoning to enable dynamic risk identification, context-aware risk prioritization, and interpretable risk mitigation recommendations. Its key innovation lies in grounding governance on user intent, thereby closing the loop across “use scenario → risk → benchmark → assessment → mitigation.” Evaluated on real-world deployments, the framework achieves 92.3% risk identification accuracy and over 85% adoption rate of mitigation recommendations, significantly enhancing the automation, operationality, and interpretability of AI governance.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Humans and AI: Intelligent User Interfaces

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be used to reveal personal information from its training set, and how a model was built; e.g., was it only trained on licensed data sets. Determining the safety of an AI system requires gathering information from a wide set of heterogeneous sources including safety benchmarks and technical documentation for the set of models used in that system. In addition, responsible use is encouraged through mechanisms that advise and help the user to take mitigating actions where safety risks are detected. We present Usage Governance Advisor which creates semi-structured governance information, identifies and prioritizes risks according to the intended use case, recommends appropriate benchmarks and risk assessments and importantly proposes mitigation strategies and actions.

Problem

• Artificial Intelligence Safety
• Ethical AI
• Risk Management in AI

Innovation

• AI Governance
• Risk Assessment
• Decision Support