# A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace

arXiv: https://arxiv.org/abs/2601.03120 · HTML: https://mlsanity.com/paper/2601.03120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs](https://mlsanity.com/paper/2410.12405/) (2024)
- [A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives](https://mlsanity.com/paper/2208.12904/) (2022)
- [The BIG Argument for AI Safety Cases](https://mlsanity.com/paper/2503.11705/) (2025)
- [Learning Generative Models for Climbing Aircraft from Radar Data](https://mlsanity.com/paper/2309.14941/) (2023)
- [Probabilistic Simulation of Aircraft Descent via a Physics-Informed Machine Learning Approach](https://mlsanity.com/paper/2504.02529/) (2025)
- [AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models](https://mlsanity.com/paper/2508.02269/) (2025)
- [Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity](https://mlsanity.com/paper/2507.13423/) (2025)

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

- [Human-in-the-Loop Testing of AI Agents for Air Traffic Control with a Regulated Assessment Framework](https://mlsanity.com/paper/2601.04288/) (2026)
- [A Future Capabilities Agent for Tactical Air Traffic Control](https://mlsanity.com/paper/2601.04285/) (2026)

## Abstract (arXiv)

Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en route UK airspace as an environment for training and testing AI Air Traffic Control (ATC) agents. There is a developing regulatory landscape for this kind of novel technology. Regulatory requirements are expected to be application specific, and may need to be tailored to each specific use case. We draw on emerging guidance for both Digital Twin development and the use of Artificial Intelligence/Machine Learning (AI/ML) in Air Traffic Management (ATM) to present an assurance framework. This framework defines actionable goals and the evidence required to demonstrate that a Digital Twin accurately represents its physical counterpart and also provides sufficient functionality across target use cases. It provides a structured approach for researchers to assess, understand and document the strengths and limitations of the Digital Twin, whilst also identifying areas where fidelity could be improved. Furthermore, it serves as a foundation for engagement with stakeholders and regulators, supporting discussions around the regulatory needs for future applications, and contributing to the emerging guidance through a concrete, working example of a Digital Twin. The framework leverages a methodology known as Trustworthy and Ethical Assurance (TEA) to develop an assurance case. An assurance case is a nested set of structured arguments that provides justified evidence for how a top-level goal has been realised. In this paper we provide an overview of each structured argument and a number of deep dives which elaborate in more detail upon particular arguments, including the required evidence, assumptions and justifications.