Fine-Grained Traceability for Transparent ML Pipelines

2026-01-21

AI Summary

This work addresses the lack of fine-grained, sample-level traceability across multi-stage machine learning pipelines in existing transparency mechanisms. The authors propose FG-Trac, a model-agnostic framework that enables verifiable, per-sample tracking without modifying model architectures or training objectives. By integrating cryptographic commitments, capturing sample lifecycle events, and computing contribution scores based on training checkpoints, FG-Trac reconstructs a complete and tamper-proof history of data usage spanning both preprocessing and training phases. Experimental results demonstrate that FG-Trac efficiently provides auditable evidence of individual sample propagation paths in convolutional neural networks and multimodal graph learning tasks, all while preserving predictive performance.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Modern machine learning systems are increasingly realised as multistage pipelines, yet existing transparency mechanisms typically operate at a model level: they describe what a system is and why it behaves as it does, but not how individual data samples are operationally recorded, tracked, and verified as they traverse the pipeline. This absence of verifiable, sample-level traceability leaves practitioners and users unable to determine whether a specific sample was used, when it was processed, or whether the corresponding records remain intact over time. We introduce FG-Trac, a model-agnostic framework that establishes verifiable, fine-grained sample-level traceability throughout machine learning pipelines. FG-Trac defines an explicit mechanism for capturing and verifying sample lifecycle events across preprocessing and training, computes contribution scores explicitly grounded in training checkpoints, and anchors these traces to tamper-evident cryptographic commitments. The framework integrates without modifying model architectures or training objectives, reconstructing complete and auditable data-usage histories with practical computational overhead. Experiments on a canonical convolutional neural network and a multimodal graph learning pipeline demonstrate that FG-Trac preserves predictive performance while enabling machine learning systems to furnish verifiable evidence of how individual samples were used and propagated during model execution.

Problem

• fine-grained traceability
• transparent ML pipelines
• sample-level traceability
• verifiable data provenance
• machine learning transparency

Innovation

• fine-grained traceability
• sample-level provenance
• cryptographic commitments
• model-agnostic framework
• verifiable ML pipelines