# Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines

arXiv: https://arxiv.org/abs/2204.11131 · HTML: https://mlsanity.com/paper/2204.11131/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 79 references that are in the map.
- First posted on arXiv 23 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Explaining by Removing: A Unified Framework for Model Explanation](https://mlsanity.com/paper/2011.14878/) (2020)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [On the Accuracy of Influence Functions for Measuring Group Effects](https://mlsanity.com/paper/1905.13289/) (2019)
- [On Second-Order Group Influence Functions for Black-Box Predictions](https://mlsanity.com/paper/1911.00418/) (2019)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions](https://mlsanity.com/paper/2005.05117/) (2020)
- [A Tensor Compiler for Unified Machine Learning Prediction Serving](https://mlsanity.com/paper/2010.04804/) (2020)
- [Towards Scalable Dataframe Systems](https://mlsanity.com/paper/2001.00888/) (2020)
- [Distributed Learning Systems with First-order Methods](https://mlsanity.com/paper/2104.05245/) (2021)
- [Complaint-driven Training Data Debugging for Query 2.0](https://mlsanity.com/paper/2004.05722/) (2020)
- [Finding Influential Training Samples for Gradient Boosted Decision Trees](https://mlsanity.com/paper/1802.06640/) (2018)
- [BAGUA: Scaling up Distributed Learning with System Relaxations](https://mlsanity.com/paper/2107.01499/) (2021)
- [Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML](https://mlsanity.com/paper/1909.00084/) (2019)
- [Data Science through the looking glass and what we found there](https://mlsanity.com/paper/1912.09536/) (2019)

## Built on by (most cited first, 17 of 17)

- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [The Journey, Not the Destination: How Data Guides Diffusion Models](https://mlsanity.com/paper/2312.06205/) (2023)
- [AI and the Problem of Knowledge Collapse](https://mlsanity.com/paper/2404.03502/) (2024)
- [A Data-Based Perspective on Transfer Learning](https://mlsanity.com/paper/2207.05739/) (2022)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation](https://mlsanity.com/paper/2308.15709/) (2023)
- [Efficient Data Shapley for Weighted Nearest Neighbor Algorithms](https://mlsanity.com/paper/2401.11103/) (2024)
- [Improving Retrieval-Augmented Large Language Models via Data Importance Learning](https://mlsanity.com/paper/2307.03027/) (2023)
- [A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"](https://mlsanity.com/paper/2304.04258/) (2023)
- [Adaptive Training Distributions with Scalable Online Bilevel Optimization](https://mlsanity.com/paper/2311.11973/) (2023)
- [2D-Shapley: A Framework for Fragmented Data Valuation](https://mlsanity.com/paper/2306.10473/) (2023)
- [Fairness-Aware Data Valuation for Supervised Learning](https://mlsanity.com/paper/2303.16963/) (2023)
- [EcoVal: An Efficient Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2402.09288/) (2024)
- [Inherent Inconsistencies of Feature Importance](https://mlsanity.com/paper/2206.08204/) (2022)
- [Pricing Strategies for Different Accuracy Models from the Same Dataset Based on Generalized Hotelling's Law](https://mlsanity.com/paper/2404.05272/) (2024)
- [ML Engineering Agents - A Data Management Perspective](https://mlsanity.com/paper/2610.16919/) (2026)

## Abstract (arXiv)

Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which training examples are 'guilty' in making the trained ML model predictions inaccurate or unfair?" Modeling data influence for ML training has attracted intensive interest over the last decade, and one popular framework is to compute the Shapley value of each training example with respect to utilities such as validation accuracy and fairness of the trained ML model. Unfortunately, despite recent intensive interest and research, existing methods only consider a single ML model "in isolation" and do not consider an end-to-end ML pipeline that consists of data transformations, feature extractors, and ML training. We present DataScope (ease.ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training. To this end, we first develop a novel algorithmic framework that computes Shapley value over a specific family of ML pipelines that we call canonical pipelines: a positive relational algebra query followed by a K-nearest-neighbor (KNN) classifier. We show that, for many subfamilies of canonical pipelines, computing Shapley value is in PTIME, contrasting the exponential complexity of computing Shapley value in general. We then put this to practice -- given an sklearn pipeline, we approximate it with a canonical pipeline to use as a proxy. We conduct extensive experiments illustrating different use cases and utilities. Our results show that DataScope is up to four orders of magnitude faster over state-of-the-art Monte Carlo-based methods, while being comparably, and often even more, effective in data debugging.