# FreaAI: Automated extraction of data slices to test machine learning models

arXiv: https://arxiv.org/abs/2108.05620 · HTML: https://mlsanity.com/paper/2108.05620/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Built on by (most cited first, 10 of 10)

- [Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing](https://mlsanity.com/paper/2201.12428/) (2022)
- [Quantitative AI Risk Assessments: Opportunities and Challenges](https://mlsanity.com/paper/2209.06317/) (2022)
- [Test & Evaluation Best Practices for Machine Learning-Enabled Systems](https://mlsanity.com/paper/2310.06800/) (2023)
- [Density-based interpretable hypercube region partitioning for mixed numeric and categorical data](https://mlsanity.com/paper/2110.05430/) (2021)
- [Detecting model drift using polynomial relations](https://mlsanity.com/paper/2110.12506/) (2021)
- [Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation](https://mlsanity.com/paper/2112.11832/) (2021)
- [Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach](https://mlsanity.com/paper/2201.00355/) (2022)
- [Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error](https://mlsanity.com/paper/2302.14616/) (2023)
- [Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors](https://mlsanity.com/paper/2305.08115/) (2023)
- [Distribution-aware Fairness Test Generation](https://mlsanity.com/paper/2305.13935/) (2023)

## Abstract (arXiv)

Machine learning (ML) solutions are prevalent. However, many challenges exist in making these solutions business-grade. One major challenge is to ensure that the ML solution provides its expected business value. In order to do that, one has to bridge the gap between the way ML model performance is measured and the solution requirements. In previous work (Barash et al, "Bridging the gap...") we demonstrated the effectiveness of utilizing feature models in bridging this gap. Whereas ML performance metrics, such as the accuracy or F1-score of a classifier, typically measure the average ML performance, feature models shed light on explainable data slices that are too far from that average, and therefore might indicate unsatisfied requirements. For example, the overall accuracy of a bank text terms classifier may be very high, say 98% ± 2%, yet it might perform poorly for terms that include short descriptions and originate from commercial accounts. A business requirement, which may be implicit in the training data, may be to perform well regardless of the type of account and length of the description. Therefore, the under-performing data slice that includes short descriptions and commercial accounts suggests poorly-met requirements. In this paper we show the feasibility of automatically extracting feature models that result in explainable data slices over which the ML solution under-performs. Our novel technique, IBM FreaAI aka FreaAI, extracts such slices from structured ML test data or any other labeled data. We demonstrate that FreaAI can automatically produce explainable and statistically-significant data slices over seven open datasets.