skip to main content
10.1145/3639475.3640103acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
research-article
Open access

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

Published: 06 June 2024 Publication History

Abstract

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.

References

[1]
Aly Sabri Abdalla and Vuk Marojevic. 2020. Machine Learning-Assisted UAV Operations with the UTM: Requirements, Challenges, and Solutions. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). 1--5.
[2]
Federal Aviation Administration. 2023. Part 107 waivers issued. Retrieved October 04, 2023 from https://www.faa.gov/uas/commercial_operators/part_107_waivers/waivers_issued
[3]
Federal Aviation Administration. 2023. UAS Data Exchange (LAANC). Retrieved 2023-10-04 from https://www.faa.gov/uas/getting_started/laanc
[4]
Federal Aviation Administration. 2023. UAS Sightings Report. Retrieved 2023-10-04 from https://www.faa.gov/uas/resources/public_records/uas_sightings_report/
[5]
Federal Aviation Administration. 2023. Unmanned Aircraft Systems (UAS) Safety Risk Management (SRM) Policy. Retrieved 2023-10-04 from https://www.faa.gov/documentLibrary/media/Order/Order_8040.6A.pdf
[6]
Federal Aviation Administration. 2023. Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Implementation Plan. Retrieved 2023-10-6 from https://www.faa.gov/sites/faa.gov/files/PL_115-254_Sec376_UAS_Traffic_Management.pdf
[7]
Aniya Aggarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha. 2019. Black Box Fairness Testing of Machine Learning Models. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Tallinn, Estonia) (ESEC/FSE 2019). Association for Computing Machinery, New York, NY, USA, 625--635.
[8]
United Kingdom Air Accidents Investigation Branch. 2023. Air Accidents Investigation Branch reports. Retrieved 2023-10-04 from https://www.gov.uk/aaib-reports?+keywords=UAS
[9]
Víctor Alarcón, Manuel García, Francisco Alarcón, Antidio Viguria, Ángel Martínez, Dominik Janisch, José Joaquín Acevedo, Ivan Maza, and Aníbal Ollero. 2020. Procedures for the integration of drones into the airspace based on U-space services. Aerospace 7, 9 (2020), 128.
[10]
Cheryl S Alexander and Henry Jay Becker. 1978. The use of vignettes in survey research. Public opinion quarterly 42, 1 (1978), 93--104.
[11]
Azza Allouch, Omar Cheikhrouhou, Anis Koubâa, Khalifa Toumi, Mohamed Khalgui, and Tuan Nguyen Gia. 2021. Utm-chain: blockchain-based secure unmanned traffic management for internet of drones. Sensors 21, 9 (2021), 3049.
[12]
Ersin Ancel, Francisco M Capristan, John V Foster, and Ryan C Condotta. 2017. Real-time risk assessment framework for unmanned aircraft system (UAS) traffic management (UTM). In 17th aiaa aviation technology, integration, and operations conference. 3273.
[13]
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2022. Machine bias. In Ethics of data and analytics. Auerbach Publications, 254--264.
[14]
Ali Aouad and Danny Segev. 2021. Display optimization for vertically differentiated locations under multinomial logit preferences. Management Science 67, 6 (2021), 3519--3550.
[15]
Christiane Atzmüller and Peter M Steiner. 2010. Experimental vignette studies in survey research. Methodology (2010).
[16]
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2017. Fairness in machine learning. Nips tutorial 1 (2017), 2017.
[17]
Cristina Barrado, Mario Boyero, Luigi Brucculeri, Giancarlo Ferrara, Andrew Hately, Peter Hullah, David Martin-Marrero, Enric Pastor, Anthony Peter Rushton, and Andreas Volkert. 2020. U-space concept of operations: A key enabler for opening airspace to emerging low-altitude operations. Aerospace 7, 3 (2020), 24.
[18]
Aleksandar Bauranov and Jasenka Rakas. 2021. Designing airspace for urban air mobility: A review of concepts and approaches. Progress in Aerospace Sciences 125 (2021), 100726.
[19]
Yuriy Brun and Alexandra Meliou. 2018. Software Fairness. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Lake Buena Vista, FL, USA) (ESEC/FSE 2018). Association for Computing Machinery, New York, NY, USA, 754--759.
[20]
Carlos Capitán, Héctor Pérez-León, Jesús Capitán, Ángel Castaño, and Aníbal Ollero. 2021. Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services. Applied Sciences 11, 9 (Apr 2021), 3995.
[21]
Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153--163.
[22]
Victoria Clarke and Virginia Braun. 2014. Thematic Analysis. Springer Netherlands, Dordrecht, 6626--6628.
[23]
John W Creswell. 1999. Mixed-method research: Introduction and application. In Handbook of educational policy. Elsevier, 455--472.
[24]
John Dawes. 2008. Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International journal of market research 50, 1 (2008), 61--104.
[25]
Christopher Decker and Paul Chiambaretto. 2022. Economic policy choices and trade-offs for Unmanned aircraft systems Traffic Management (UTM): Insights from Europe and the United States. Transportation research part A: policy and practice 157 (2022), 40--58.
[26]
DeDrone. [n. d.]. Map of global drone incidents by Dedrone Anti-Drone. Retrieved 2023-10-04 from https://www.dedrone.com/resources/incidents-new/all
[27]
Milan Erdelj, Enrico Natalizio, Kaushik R. Chowdhury, and Ian F. Akyildiz. 2017. Help from the Sky: Leveraging UAVs for Disaster Management. IEEE Pervasive Computing 16 (2017), 24--32. https://api.semanticscholar.org/CorpusID:18047608
[28]
Center for the Study of the Drone at Bard College. 2019. Drone sightings and close encounters: An analysis. Retrieved 2023-10-04 from https://dronecenter.bard.edu/projects/other-projects/drone-sightings-and-close-encounters/
[29]
Catherine Gross. 2007. Community perspectives of wind energy in Australia: The application of a justice and community fairness framework to increase social acceptance. Energy Policy 35 (2007), 2727--2736. https://api.semanticscholar.org/CorpusID:153931705
[30]
Matt Grote, Aliaksei Pilko, James Scanlan, Tom Cherrett, Janet Dickinson, Angela Smith, Andrew Oakey, and Greg Marsden. 2022. Sharing airspace with Uncrewed Aerial Vehicles (UAVs): Views of the General Aviation (GA) community. Journal of Air Transport Management 102 (2022), 102218.
[31]
Mariam Guizani, Bianca Trinkenreich, Aileen Abril Castro-Guzman, Igor Steinmacher, Marco Gerosa, and Anita Sarma. 2022. Perceptions of the State of D&I and D&I Initiative in the ASF. In Proceedings of the 2022 ACM/IEEE 44th International Conference on Software Engineering: Software Engineering in Society (Pittsburgh, Pennsylvania) (ICSE-SEIS '22). Association for Computing Machinery, New York, NY, USA, 130--142.
[32]
Mostafa Hassanalian and Abdessattar Abdelkefi. 2017. Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences 91 (2017), 99--131.
[33]
Monika Hengstler, Ellen Enkel, and Selina Duelli. 2016. Applied artificial intelligence and trust---The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change 105 (2016), 105--120.
[34]
Tao Jiang, Jared Geller, Daiheng Ni, and John Collura. 2016. Unmanned Aircraft System traffic management: Concept of operation and system architecture. International journal of transportation science and technology 5, 3 (2016), 123--135.
[35]
Parimal Kopardekar. 2019. Unmanned aircraft systems traffic management. US Patent 10,332,405.
[36]
Nancy Leveson. 2011. Engineering a Safer World: Systems Thinking Applied to Safety. MIT Press.
[37]
Sebastian Linxen, Christian Sturm, Florian Brühlmann, Vincent Cassau, Klaus Opwis, and Katharina Reinecke. 2021. How WEIRD is CHI?. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 143, 14 pages.
[38]
Mingnan Liu, Fred Conrad, and Sunghee Lee. 2017. Comparing acquiescent and extreme response styles in face-to-face and web surveys. Quality Quantity 51 (03 2017).
[39]
Chiara Longoni, Andrea Bonezzi, and Carey K Morewedge. 2019. Resistance to medical artificial intelligence. Journal of Consumer Research 46, 4 (2019), 629--650.
[40]
Lisa Matsuyama, Rileigh Zimmerman, Casey Eaton, Kristin Weger, Bryan Mesmer, Nathan Tenhundfeld, Douglas Van Bossuyt, and Robert Semmens. 2021. Determinants that are believed to influence the acceptance and adoption of mission critical autonomous systems. In AIAA Scitech 2021 Forum. 1156.
[41]
Andrew McNamara, Justin Smith, and Emerson Murphy-Hill. 2018. Does ACM's Code of Ethics Change Ethical Decision Making in Software Development?. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Lake Buena Vista, FL, USA) (ESEC/FSE 2018). Association for Computing Machinery, New York, NY, USA, 729--733.
[42]
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR) 54, 6 (2021), 1--35.
[43]
Rico Merkert, Matthew J Beck, and James Bushell. 2021. Will It Fly? Adoption of the road pricing framework to manage drone use of airspace. Transportation Research Part A: Policy and Practice 150 (2021), 156--170.
[44]
Rico Merkert and James Bushell. 2020. Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control. Journal of air transport management 89 (2020), 101929.
[45]
Arthur Holland Michael and Dan Gettinger. 2017. Drone Incidents: A Survey of Legal Cases. Retrieved 2023-10-04 from https://dronecenter.bard.edu/files/2017/04/CSD-Drone-Incidents.pdf
[46]
Matthew B Miles and A Michael Huberman. 1994. Qualitative data analysis: An expanded sourcebook. sage.
[47]
Nikolaos Mittas and Lefteris Angelis. 2013. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm. IEEE Transactions on Software Engineering 39, 4 (2013), 537--551.
[48]
International Civil Aviation Organization. 2023. Unmanned Aircraft Systems Traffic Management (UTM) - A Common Framework with Core Principles for Global Harmonization. Retrieved 2023-10-04 from https://www.icao.int/safety/UA/Documents/UTM%20Framework%20Edition%204.pdf
[49]
Roel Popping. 2015. Analyzing Open-ended Questions by Means of Text Analysis Procedures. Bulletin de méthodologie sociologique: BMS 128 (10 2015), 23--39.
[50]
Thomas Prevot, Joseph Rios, Parimal Kopardekar, John Robinson III, Marcus Johnson, and Jaewoo Jung. 2016. UAS Traffic Management (UTM) Concept of Operations to Safely Enable Low Altitude Flight Operations.
[51]
Shilin Qiu, Qihe Liu, Shijie Zhou, and Chunjiang Wu. 2019. Review of artificial intelligence adversarial attack and defense technologies. Applied Sciences 9, 5 (2019), 909.
[52]
Kelsey Reichmann. 2021. Airwayz AI-powered unmanned traffic management put to the test in israel drone pilot program. Retrieved 2023-10-04 from https://www.aviationtoday.com/2021/04/12/airwayz-ai-powered-unmanned-traffic-management-put-test-israel-drone-pilot-program/
[53]
Kelsey Reichmann. 2021. Airwayz AI-Powered Unmanned Traffic Management Put to the Test in Israel Drone Pilot Program. Retrieved 2023-10-04 from https://www.militaryaerospace.com/commercial-aerospace/article/14232227/airwayz-aibased-systems-to-spearhead-usecase-of-multiple-drone-fleets-in-urban-airspace
[54]
Catherine A Roster, Lorenzo Lucianetti, and Gerald Albaum. 2015. Exploring slider vs. categorical response formats in web-based surveys. Journal of Research Practice 11, 1 (2015), D1--D1.
[55]
JU SESAR. 2016. European drones outlook study unlocking the value for europe. Siebert, JU, Nov (2016).
[56]
Hazim Shakhatreh, Ahmad H. Sawalmeh, Ala Al-Fuqaha, Zuochao Dou, Eyad Almaita, Issa Khalil, Noor Shamsiah Othman, Abdallah Khreishah, and Mohsen Guizani. 2019. Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges. IEEE Access 7 (2019), 48572--48634.
[57]
Aviation Safety Reporting System. 2023. Reports involving Unmanned Aircraft Systems (UAS) events reported by operators of manned or unmanned aircraft. Retrieved 2023-10-04 from https://asrs.arc.nasa.gov/docs/rpsts/uas.pdf
[58]
Michael Vierhauser, Md Nafee Al Islam, Ankit Agrawal, Jane Cleland-Huang, and James Mason. 2021. Hazard Analysis for Human-on-the-Loop Interactions in SUAS Systems. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Athens, Greece) (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 8--19.
[59]
Ryan J Wallace, Kristy W Kiernan, Tom Haritos, John Robbins, and Godfrey V D'souza. 2018. Evaluating small UAS near midair collision risk using AeroScope and ADS-B. International Journal of Aviation, Aeronautics, and Aerospace 5, 4 (2018), 2.
[60]
Lanier Watkins, Nick Sarfaraz, Sebastian Zanlongo, Joshua Silbermann, Tyler Young, and Randall Sleight. 2021. An Investigative Study Into An Autonomous UAS Traffic Management System For Congested Airspace Safety. In 2021 IEEE International Conference on Communications Workshops (ICC Workshops). 1--6.
[61]
Kristin Weger, Lisa Matsuyama, Rileigh Zimmermann, Bryan Mesmer, Douglas Van Bossuyt, Rob Semmens, and Casey Eaton. 2022. Insight into User Acceptance and Adoption of Autonomous Systems in Mission Critical Environments. International Journal of Human-Computer Interaction 39 (06 2022), 1--15.
[62]
Wikipedia. 2023. List of unmanned aerial vehicle-related incidents. Retrieved 2023-10-04 from https://en.wikipedia.org/wiki/List_of_unmanned_aerial_vehicle-related_incidents
[63]
GUAN Xiangmin, LYU Renli, SHI Hongxia, and CHEN Jun. 2020. A survey of safety separation management and collision avoidance approaches of civil UAS operating in integration national airspace system. Chinese Journal of Aeronautics 33, 11 (2020), 2851--2863.
[64]
Na Zhang, Hu Liu, Bing Feng Ng, and Kin Huat Low. 2020. Collision probability between intruding drone and commercial aircraft in airport restricted area based on collision-course trajectory planning. Transportation research part C: emerging technologies 120 (2020), 102736.

Cited By

View all

Index Terms

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

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ICSE-SEIS'24: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society
    April 2024
    210 pages
    ISBN:9798400704994
    DOI:10.1145/3639475
    This work is licensed under a Creative Commons Attribution International 4.0 License.

    Sponsors

    In-Cooperation

    • Faculty of Engineering of University of Porto

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 June 2024

    Check for updates

    Author Tags

    1. drones
    2. sUAS
    3. fairness
    4. machine learning
    5. software engineering

    Qualifiers

    • Research-article

    Funding Sources

    • National Aeronautics and Space Administration

    Conference

    ICSE-SEIS'24
    Sponsor:

    Upcoming Conference

    ICSE 2025

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)184
    • Downloads (Last 6 weeks)51
    Reflects downloads up to 09 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media