# Fine-Grained Traceability for Transparent ML Pipelines

arXiv: https://arxiv.org/abs/2601.14971 · HTML: https://mlsanity.com/paper/2601.14971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Estimating Training Data Influence by Tracing Gradient Descent](https://mlsanity.com/paper/2002.08484/) (2020)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows](https://mlsanity.com/paper/2508.02866/) (2025)
- [GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis](https://mlsanity.com/paper/2203.09034/) (2022)
- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)
- [MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction](https://mlsanity.com/paper/2406.14455/) (2024)
- [DAG Card is the new Model Card](https://mlsanity.com/paper/2110.13601/) (2021)
- [Instance-Level Data-Use Auditing of Visual ML Models](https://mlsanity.com/paper/2503.22413/) (2025)
- [From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP](https://mlsanity.com/paper/2504.02064/) (2025)

## Built on by (most cited first, 1 of 1)

- [OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets](https://mlsanity.com/paper/2607.13037/) (2026)

## Abstract (arXiv)

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.