Provenance-Enabled Explainable AI
Summary: PXAI decouples XAI from ML models via a provenance graph that tracks data creation and transformation throughout the model. Prunes irrelevant variables and computations to accelerate explanations; case studies show efficiency gains for complex models. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiachi Zhang (Alibaba)
- 2. Wenchao Zhou (Alibaba)
- 3. Benjamin E. Ujcich (Georgetown University)
BibTeX Citation
@inproceedings{zhang_sigmod24,
title = {{Provenance-Enabled Explainable AI}},
author = {Zhang, Jiachi and Zhou, Wenchao and Ujcich, Benjamin E.},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3698826},
url = {https://dl.acm.org/doi/10.1145/3698826},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 17 | Provenance Semirings | 2007 | PODS | 0.00059843817 |
| 1,569 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010335423 |
| 1,627 | Efficient Provenance Storage | 2008 | SIGMOD | 0.00010188097 |
| 1,752 | Approximate Lineage for Probabilistic Databases | 2008 | VLDB | 9.8358116e-05 |
| 2,431 | noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts | 2017 | VLDB | 8.5875984e-05 |
| 4,130 | PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models | 2020 | SIGMOD | 6.8839621e-05 |
| 4,240 | LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems | 2021 | SIGMOD | 6.809685e-05 |
| 6,071 | Fine-Grained Lineage for Safer Notebook Interactions | 2021 | VLDB | 5.9873765e-05 |
| 6,587 | Selective Provenance for Datalog Programs Using Top-K Queries | 2015 | VLDB | 5.8333865e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,509 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD |
| 2 | 10,825 | Explaining Black-Box Clustering Pipelines With Cluster-Explorer | 2025 | VLDB |
| 3 | 6,360 | Approximate Summaries for Why and Why-not Provenance | 2020 | VLDB |
| 4 | 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD |
| 5 | 8,050 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB |
| 6 | 628 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB |
| 7 | 4,602 | Provenance for Natural Language Queries | 2017 | VLDB |
| 8 | 13,462 | Demonstration of Generating Explanations for Black-Box Algorithms Using Lewis | 2021 | VLDB |
| 9 | 5,054 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD |
| 10 | 3,000 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD |