Enterprise AI Analysis
Provenance Question-based AI Transparency and Accountable AI Governance
Ensuring transparency in Artificial Intelligence (AI) systems is critical for building trust and accountability. However, implementing technical governance and transparency in complex AI systems remains a challenge due to vague requirements, missing know-how and time resources. Provenance questions (PQs), outlining transparency requirements of a system, can play a key role in counteracting this. Nevertheless, the implementation of technical transparency and suitable PQs in complex AI systems pose significant challenges. This paper presents an approach for the formalization and transformation of PQs, aimed at improving AI system transparency. This involves a question analysis on a linguistic and provenance level, based on the W7 model. To this end, we propose two definitions for simple and complex PQs to map them to PROV-O concepts, followed by a discussion of a reference architecture.
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The presented PQ framework consists of four components: (1) question selection, (2) linguistic analysis of provenance questions, (3) mapping to provenance concepts, and (4) provenance data collection & analysis. In the first component, users can upload a machine-readable AI system description and either loading applicable PQs from an existing provenance questionbank or providing custom PQs in natural language. After that, we propose analyzing the provenance questions using natural language processing (NLP) and the W7 model (question categorization). In the third component, the linguistically analyzed PQs are formalized and mapped to concepts from provenance data models such as P-Plan or PROV-O. Finally, the provenance trace templates (key-value-pairs for required log information) are generated. These templates are used to derive provenance traces during the runtime of the AI system. These provenance traces are finally integrated and stored for retrieval. We are showing an example transformation for a simple PQ including a question word, an object and a (main) verb.
We propose a PQ support framework to address provenance-related challenges in technical governance aiming to integrate AI system workflow descriptions with a provenance question bank. This framework transforms applicable natural language questions to formalized PQs, and then derives trace templates to guide stakeholders in implementing provenance by design. Our approach enhances AI system transparency through structured audit trace collection and improves the quality of provenance logs and traces. Based on these findings, we will develop a prototype and extend our concept to complex PQs and those that do not meet our current definition. To build the question bank, we need application-specific PQs. We collect these through an (extensive) literature review and an online survey. In the survey, we ask application experts and provenance experts about PQs for their domain. We will initially focus on one AI context. The paper [1] was presented as part of ProvenanceWeek2025 Best of the Rest Track. Laura Waltersdorfer is funded by the European Union's Horizon research grant 101120323. [1] L. Waltersdorfer, D. Hausler, and T. Auge. 2025. Provenance Question-based AI Transparency and Accountable AI Governance. In AAAI 2025 Workshop on AI Governance: Alignment, Morality, and Law. https://openreview.net/forum?id=EFsyy6DqCM
Provenance Question Transformation Flow
Natural Language PQ
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Linguistic Analysis of PQ
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Rewritten PQ
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Formalization of PQ
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Mapping to PROV-O
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Provenance Trace Template
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Provenance Trace
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Provenance Result
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A clear path to integrating robust AI transparency and accountability within your enterprise. Our phased approach ensures a smooth transition and measurable success.
Phase 1: Discovery & Assessment (1-2 Weeks)
Comprehensive review of existing AI systems, data pipelines, and governance frameworks. Identification of key transparency requirements and potential compliance gaps. Stakeholder interviews and initial roadmap drafting.
Phase 2: Framework Customization & Integration (3-5 Weeks)
Tailoring the PQ framework to your specific AI models and business processes. Integration of provenance data collection mechanisms into existing infrastructure. Development of custom trace templates.
Phase 3: Pilot Deployment & Optimization (2-3 Weeks)
Deployment of the enhanced transparency solution on a pilot AI system. Collection and analysis of initial provenance traces. Iterative refinement and optimization based on real-world performance and feedback.
Phase 4: Full-Scale Rollout & Training (Ongoing)
Expansion of the solution across all relevant AI systems. Comprehensive training for your teams on using the new governance tools and interpreting provenance data. Establishment of continuous monitoring and reporting.
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