# Four Principles of Explainable AI as Applied to Biometrics and Facial Forensic Algorithms

arXiv: https://arxiv.org/abs/2002.01014 · HTML: https://mlsanity.com/paper/2002.01014/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Discriminative Deep Feature Visualization for Explainable Face Recognition](https://mlsanity.com/paper/2306.00402/) (2023)
- [An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detection](https://mlsanity.com/paper/2304.14509/) (2023)
- [Aligning Characteristic Descriptors with Images for Human-Expert-like Explainability](https://mlsanity.com/paper/2411.04008/) (2024)

## Abstract (arXiv)

Traditionally, researchers in automatic face recognition and biometric technologies have focused on developing accurate algorithms. With this technology being integrated into operational systems, engineers and scientists are being asked, do these systems meet societal norms? The origin of this line of inquiry is `trust' of artificial intelligence (AI) systems. In this paper, we concentrate on adapting explainable AI to face recognition and biometrics, and we present four principles of explainable AI to face recognition and biometrics. The principles are illustrated by itfour case studies, which show the challenges and issues in developing algorithms that can produce explanations.