# Outline of an Independent Systematic Blackbox Test for ML-based Systems

arXiv: https://arxiv.org/abs/2401.17062 · HTML: https://mlsanity.com/paper/2401.17062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems](https://mlsanity.com/paper/1803.07519/) (2018)
- [Introduction to Neural Network Verification](https://mlsanity.com/paper/2109.10317/) (2021)
- [Feature-Guided Black-Box Safety Testing of Deep Neural Networks](https://mlsanity.com/paper/1710.07859/) (2017)
- [Scenic: A Language for Scenario Specification and Data Generation](https://mlsanity.com/paper/2010.06580/) (2020)
- [Grid Saliency for Context Explanations of Semantic Segmentation](https://mlsanity.com/paper/1907.13054/) (2019)
- [Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches](https://mlsanity.com/paper/2102.05897/) (2021)
- [Identification of Systematic Errors of Image Classifiers on Rare Subgroups](https://mlsanity.com/paper/2303.05072/) (2023)
- [One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving](https://mlsanity.com/paper/2209.00342/) (2022)
- [Towards Dependability Metrics for Neural Networks](https://mlsanity.com/paper/1806.02338/) (2018)
- [Certified Control: An Architecture for Verifiable Safety of Autonomous Vehicles](https://mlsanity.com/paper/2104.06178/) (2021)
- [A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving](https://mlsanity.com/paper/2304.10837/) (2023)
- [A Stochastic Approach to Classification Error Estimates in Convolutional Neural Networks](https://mlsanity.com/paper/2401.06156/) (2023)

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

- [Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems](https://mlsanity.com/paper/2609.24397/) (2026)

## Abstract (arXiv)

This article proposes a test procedure that can be used to test ML models and ML-based systems independently of the actual training process. In this way, the typical quality statements such as accuracy and precision of these models and system can be verified independently, taking into account their black box character and the immanent stochastic properties of ML models and their training data. The article presents first results from a set of test experiments and suggest extensions to existing test methods reflecting the stochastic nature of ML models and ML-based systems.