# Color Teams for Machine Learning Development

arXiv: https://arxiv.org/abs/2110.10601 · HTML: https://mlsanity.com/paper/2110.10601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [Adversarial Machine Learning: Bayesian Perspectives](https://mlsanity.com/paper/2003.03546/) (2020)
- [On managing vulnerabilities in AI/ML systems](https://mlsanity.com/paper/2101.10865/) (2021)
- [Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models](https://mlsanity.com/paper/2006.11130/) (2020)
- [The Concept of Cyber Defence Exercises (CDX): Planning, Execution, Evaluation](https://mlsanity.com/paper/1906.03184/) (2019)
- [A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models](https://mlsanity.com/paper/2103.02718/) (2021)

## Abstract (arXiv)

Machine learning and software development share processes and methodologies for reliably delivering products to customers. This work proposes the use of a new teaming construct for forming machine learning teams for better combatting adversarial attackers. In cybersecurity, infrastructure uses these teams to protect their systems by using system builders and programmers to also offer more robustness to their platforms. Color teams provide clear responsibility to the individuals on each team for which part of the baseline (Yellow), attack (Red), and defense (Blue) breakout of the pipeline. Combining colors leads to additional knowledge shared across the team and more robust models built during development. The responsibilities of the new teams Orange, Green, and Purple will be outlined during this paper along with an overview of the necessary resources for these teams to be successful.