# The Promise and Peril of Artificial Intelligence -- Violet Teaming Offers a Balanced Path Forward

arXiv: https://arxiv.org/abs/2308.14253 · HTML: https://mlsanity.com/paper/2308.14253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 92 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Toxicity Detection: Does Context Really Matter?](https://mlsanity.com/paper/2006.00998/) (2020)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models](https://mlsanity.com/paper/2211.14946/) (2022)
- [PanGu-Sigma: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing](https://mlsanity.com/paper/2303.10845/) (2023)
- [AGI Safety Literature Review](https://mlsanity.com/paper/1805.01109/) (2018)
- [SIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases](https://mlsanity.com/paper/1803.09565/) (2018)

## Built on by (most cited first, 1 of 1)

- [Red Teaming AI Red Teaming](https://mlsanity.com/paper/2507.05538/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) promises immense benefits across sectors, yet also poses risks from dual-use potentials, biases, and unintended behaviors. This paper reviews emerging issues with opaque and uncontrollable AI systems and proposes an integrative framework called violet teaming to develop reliable and responsible AI. Violet teaming combines adversarial vulnerability probing (red teaming) with solutions for safety and security (blue teaming) while prioritizing ethics and social benefit. It emerged from AI safety research to manage risks proactively by design. The paper traces the evolution of red, blue, and purple teaming toward violet teaming, and then discusses applying violet techniques to address biosecurity risks of AI in biotechnology. Additional sections review key perspectives across law, ethics, cybersecurity, macrostrategy, and industry best practices essential for operationalizing responsible AI through holistic technical and social considerations. Violet teaming provides both philosophy and method for steering AI trajectories toward societal good. With conscience and wisdom, the extraordinary capabilities of AI can enrich humanity. But without adequate precaution, the risks could prove catastrophic. Violet teaming aims to empower moral technology for the common welfare.