# The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs

arXiv: https://arxiv.org/abs/2009.05260 · HTML: https://mlsanity.com/paper/2009.05260/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Bugs in Machine Learning-based Systems: A Faultload Benchmark](https://mlsanity.com/paper/2206.12311/) (2022)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?](https://mlsanity.com/paper/2103.01837/) (2021)
- [Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications](https://mlsanity.com/paper/2203.16225/) (2022)
- [Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice](https://mlsanity.com/paper/2203.15414/) (2022)

## Abstract (arXiv)

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.