# An AI Architecture with the Capability to Explain Recognition Results

arXiv: https://arxiv.org/abs/2406.08740 · HTML: https://mlsanity.com/paper/2406.08740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 13 Jun 2024.

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

- [Explainable Neural Networks based on Additive Index Models](https://mlsanity.com/paper/1806.01933/) (2018)
- [Insights into Performance Fitness and Error Metrics for Machine Learning](https://mlsanity.com/paper/2006.00887/) (2020)

## Built on by (most cited first, 2 of 2)

- [An AI Architecture with the Capability to Classify and Explain Hardware Trojans](https://mlsanity.com/paper/2407.04551/) (2024)
- [Explainability Methods for Hardware Trojan Detection: A Systematic Comparison](https://mlsanity.com/paper/2601.18696/) (2026)

## Abstract (arXiv)

Explainability is needed to establish confidence in machine learning results. Some explainable methods take a post hoc approach to explain the weights of machine learning models, others highlight areas of the input contributing to decisions. These methods do not adequately explain decisions, in plain terms. Explainable property-based systems have been shown to provide explanations in plain terms, however, they have not performed as well as leading unexplainable machine learning methods. This research focuses on the importance of metrics to explainability and contributes two methods yielding performance gains. The first method introduces a combination of explainable and unexplainable flows, proposing a metric to characterize explainability of a decision. The second method compares classic metrics for estimating the effectiveness of neural networks in the system, posing a new metric as the leading performer. Results from the new methods and examples from handwritten datasets are presented.