AI2030 Responsible AI Tools Marketplace

Key Takeaways

LLM-FACETS addresses a critical accessibility gap in AI auditing: The framework makes LLM evaluation accessible to non-technical practitioners like domain experts and compliance officers through a browser interface, removing barriers that previously required programming expertise and complex setup—democratizing oversight responsibilities outlined in the EU AI Act and NIST frameworks.
Privacy protection is built into the evaluation architecture: The system keeps sensitive evaluation data on self-hosted servers rather than transmitting it to external services, with deterministic metrics (BLEU, ROUGE, BERTScore) running entirely locally and users retaining full control over credentials when external APIs are used—addressing legal compliance concerns for organizations responsible for AI oversight.
The framework operationalizes three key dimensions of responsible AI: LLM-FACETS evaluates factual grounding, epistemic calibration (uncertainty quantification), and reproducibility through specific mechanisms including token-level probability visualization, multi-judge consensus to reduce bias, and RAG Triad metrics designed to detect and locate hallucinations in model outputs.
The design reflects real stakeholder needs across the AI governance ecosystem: By structuring the framework around three practitioner profiles (technical experts, domain experts, compliance officers), the tool acknowledges that different roles need different capabilities—moving beyond one-size-fits-all evaluation approaches to support the diverse responsibilities outlined in major AI governance frameworks.

Abstract

Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite for responsible AI deployment. Yet auditing LLMs remains inaccessible to non-technical practitioners: existing tools require programming expertise and non-trivial environment setup, and cloud-hosted platforms transmit evaluation data to external services, creating barriers for domain experts and compliance officers legally responsible for AI oversight. We introduce LLM-FACETS (LLM FActuality Cross-EvaluaTion System): an open-source framework with a browser-accessible interface and a plugin architecture, structured around three practitioner profiles (technical experts, domain experts, compliance officers) that mirror the stakeholder categories identified in the EU AI Act and the NIST AI Risk Management Framework. The architecture makes data flows explicit: deterministic metrics (BLEU, ROUGE, BERTScore) run entirely within the self-hosted server with no outbound transmission; LLM-judge metrics contact external APIs explicitly, with users retaining full credential control. The framework operationalizes transparency through three mechanisms: token-level log-probability visualization for epistemic uncertainty, multi-judge consensus to mitigate judge bias, and RAG Triad metrics (Faithfulness, Answer Relevance, Context Relevance) to detect and localize hallucinations. A plugin architecture allows any new metric or dataset to be integrated without modifying the evaluation pipeline. The open-source implementation enables cross-checking across multiple metrics targeting the same property, ensuring reproducibility and decoupling AI accountability from the teams building the systems assessed. We verify the framework through cross-validation of 18 metric implementations against canonical reference libraries.

Submitted to ACM Journal on Responsible Computing, Special Section: Collaborative Methods and Tools for Engineering and Evaluating Transparency in AI. 28 pages 9 figures, 7 tables, 1 algorithm. Source code: https://github.com/Scriptor-Group/AIMVi

Key Takeaways

LLM-FACETS addresses a critical accessibility gap in AI auditing: The framework makes LLM evaluation accessible to non-technical practitioners like domain experts and compliance officers through a browser interface, removing barriers that previously required programming expertise and complex setup—democratizing oversight responsibilities outlined in the EU AI Act and NIST frameworks.
Privacy protection is built into the evaluation architecture: The system keeps sensitive evaluation data on self-hosted servers rather than transmitting it to external services, with deterministic metrics (BLEU, ROUGE, BERTScore) running entirely locally and users retaining full control over credentials when external APIs are used—addressing legal compliance concerns for organizations responsible for AI oversight.
The framework operationalizes three key dimensions of responsible AI: LLM-FACETS evaluates factual grounding, epistemic calibration (uncertainty quantification), and reproducibility through specific mechanisms including token-level probability visualization, multi-judge consensus to reduce bias, and RAG Triad metrics designed to detect and locate hallucinations in model outputs.
The design reflects real stakeholder needs across the AI governance ecosystem: By structuring the framework around three practitioner profiles (technical experts, domain experts, compliance officers), the tool acknowledges that different roles need different capabilities—moving beyond one-size-fits-all evaluation approaches to support the diverse responsibilities outlined in major AI governance frameworks.

Abstract

LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links