# Principles for Evaluation of AI/ML Model Performance and Robustness

arXiv: https://arxiv.org/abs/2107.02868 · HTML: https://mlsanity.com/paper/2107.02868/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)

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

- [Towards Modeling Data Quality and Machine Learning Model Performance](https://mlsanity.com/paper/2412.05882/) (2024)
- [System Analysis for Responsible Design of Modern AI/ML Systems](https://mlsanity.com/paper/2204.08836/) (2022)

## Abstract (arXiv)

The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to be brittle and nonrobust. In a complex and ever-changing national security environment, it is vital that the DoD establish a sound and methodical process to evaluate the performance and robustness of AI/ML models before these new capabilities are deployed to the field. This paper reviews the AI/ML development process, highlights common best practices for AI/ML model evaluation, and makes recommendations to DoD evaluators to ensure the deployment of robust AI/ML capabilities for national security needs.