# Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control

arXiv: https://arxiv.org/abs/2311.08943 · HTML: https://mlsanity.com/paper/2311.08943/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 117 references that are in the map.
- First posted on arXiv 15 Nov 2023.

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

- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Deep Neural Network Compression for Aircraft Collision Avoidance Systems](https://mlsanity.com/paper/1810.04240/) (2018)
- [Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems](https://mlsanity.com/paper/2110.03506/) (2021)
- [Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.04244/) (2018)
- [Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization](https://mlsanity.com/paper/1911.05478/) (2019)
- [Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning](https://mlsanity.com/paper/1904.09865/) (2019)
- [Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning](https://mlsanity.com/paper/2008.03215/) (2020)

## Abstract (arXiv)

Designing a safe, trusted, and ethical AI may be practically impossible; however, designing AI with safe, trusted, and ethical use in mind is possible and necessary in safety and mission-critical domains like aerospace. Safe, trusted, and ethical use of AI are often used interchangeably; however, a system can be safely used but not trusted or ethical, have a trusted use that is not safe or ethical, and have an ethical use that is not safe or trusted. This manuscript serves as a primer to illuminate the nuanced differences between these concepts, with a specific focus on applications of Human-AI teaming in aerospace system control, where humans may be in, on, or out-of-the-loop of decision-making.