# Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations

arXiv: https://arxiv.org/abs/2401.07353 · HTML: https://mlsanity.com/paper/2401.07353/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 14 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Machine Learning-Assisted UAV Operations with UTM: Requirements, Challenges, and Solutions](https://mlsanity.com/paper/2006.14544/) (2020)
- [Perceptions of the State of D&I and D&I Initiative in the ASF](https://mlsanity.com/paper/2202.13260/) (2022)

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

- [CoDefeater: Using LLMs To Find Defeaters in Assurance Cases](https://mlsanity.com/paper/2407.13717/) (2024)
- [Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges](https://mlsanity.com/paper/2406.06736/) (2024)

## Abstract (arXiv)

Small Unmanned Aircraft Systems (sUAS) have gained widespread adoption across a diverse range of applications. This has introduced operational complexities within shared airspaces and an increase in reported incidents, raising safety concerns. In response, the U.S. Federal Aviation Administration (FAA) is developing a UAS Traffic Management (UTM) system to control access to airspace based on an sUAS's predicted ability to safely complete its mission. However, a fully automated system capable of swiftly approving or denying flight requests can be prone to bias and must consider safety, transparency, and fairness to diverse stakeholders. In this paper, we present an initial study that explores stakeholders' perspectives on factors that should be considered in an automated system. Results indicate flight characteristics and environmental conditions were perceived as most important but pilot and drone capabilities should also be considered. Further, several respondents indicated an aversion to any AI-supported automation, highlighting the need for full transparency in automated decision-making. Results provide a societal perspective on the challenges of automating UTM flight authorization decisions and help frame the ongoing design of a solution acceptable to the broader sUAS community.