# Machine Learning Model Drift Detection Via Weak Data Slices

arXiv: https://arxiv.org/abs/2108.05319 · HTML: https://mlsanity.com/paper/2108.05319/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Detection of data drift and outliers affecting machine learning model performance over time](https://mlsanity.com/paper/2012.09258/) (2020)

## Built on by (most cited first, 6 of 6)

- [Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations](https://mlsanity.com/paper/2403.06009/) (2024)
- [Endogenous Macrodynamics in Algorithmic Recourse](https://mlsanity.com/paper/2308.08187/) (2023)
- [Detecting model drift using polynomial relations](https://mlsanity.com/paper/2110.12506/) (2021)
- [Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach](https://mlsanity.com/paper/2201.00355/) (2022)
- [Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors](https://mlsanity.com/paper/2305.08115/) (2023)
- [ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values](https://mlsanity.com/paper/2604.11200/) (2026)

## Abstract (arXiv)

Detecting drift in performance of Machine Learning (ML) models is an acknowledged challenge. For ML models to become an integral part of business applications it is essential to detect when an ML model drifts away from acceptable operation. However, it is often the case that actual labels are difficult and expensive to get, for example, because they require expert judgment. Therefore, there is a need for methods that detect likely degradation in ML operation without labels. We propose a method that utilizes feature space rules, called data slices, for drift detection. We provide experimental indications that our method is likely to identify that the ML model will likely change in performance, based on changes in the underlying data.