# Towards Improved Research Methodologies for Industrial AI: A case study of false call reduction

arXiv: https://arxiv.org/abs/2506.14521 · HTML: https://mlsanity.com/paper/2506.14521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Scalable Learning of Non-Decomposable Objectives](https://mlsanity.com/paper/1608.04802/) (2016)
- [Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology](https://mlsanity.com/paper/2001.09612/) (2020)
- [Data-Driven Prediction Model of Components Shift during Reflow Process in Surface Mount Technology](https://mlsanity.com/paper/2001.09619/) (2020)

## Abstract (arXiv)

Are current artificial intelligence (AI) research methodologies ready to create successful, productive, and profitable AI applications? This work presents a case study on an industrial AI use case called false call reduction for automated optical inspection to demonstrate the shortcomings of current best practices. We identify seven weaknesses prevalent in related peer-reviewed work and experimentally show their consequences. We show that the best-practice methodology would fail for this use case. We argue amongst others for the necessity of requirement-aware metrics to ensure achieving business objectives, clear definitions of success criteria, and a thorough analysis of temporal dynamics in experimental datasets. Our work encourages researchers to critically assess their methodologies for more successful applied AI research.