Trustworthy Human-Autonomy Teaming for Proportionality Assessment in Military Operations

被引:0
|
作者
Maathuis, Clara [1 ]
机构
[1] Open Univ Netherlands, Heerlen, Netherlands
关键词
trustworthy AI; responsible AI; human-autonomy teaming; targeting; military operations;
D O I
10.1109/ICAPAI61893.2024.10541173
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Over the past decades, rapid technological advancements resulted in the integration of autonomous systems and AI across various societal domains. An emerging paradigm in this realm is the human-autonomy teaming which merges human efforts and intelligence with efficiency and performance of autonomous systems through collaboration for reaching common goals and leveraging their strengths. Building such systems should be done in a safe, responsible, and reliable manner, thus in a trustworthy way. While efforts for developing such systems exist in the military domain, they are mainly tackling the technical dimension involved in tasks like reconnaissance and target engagement, and less in conjunction with other dimensions like ethical and legal while focusing on possible produced effects. The intended effects contributing to achieving military objectives represent military advantage and the unintended effects on civilian and civilian objects represent collateral damage. Bringing and assessing these types of effects in a single instance is done through the proportionality assessment which represents the pilar when conducting military operations. Nevertheless, no previous efforts are dedicated to building human-autonomy teaming systems in the context of proportionality assessment in military operations in a trustworthy way. Hence, it is the aim of this research to define this concept and propose a corresponding design framework on this behalf with the intention to contribute to building safe, responsible, and reliable systems in the military domain. To achieve this goal, the Design Science Research methodology is followed in a Value Sensitive Design approach based on extensive research of relevant studies and field experience.
引用
收藏
页码:89 / 96
页数:8
相关论文
共 50 条
  • [1] Human-autonomy teaming in military settings
    Chen, Jessie Y. C.
    THEORETICAL ISSUES IN ERGONOMICS SCIENCE, 2018, 19 (03) : 255 - 258
  • [2] Trusting machine intelligence: artificial intelligence and human-autonomy teaming in military operations
    Mayer, Michael
    DEFENCE AND SECURITY ANALYSIS, 2023, 39 (04): : 521 - 538
  • [3] Special issue on “Human-Autonomy Teaming in Military Contexts”
    Jessie Chen
    Axel Schulte
    Human-Intelligent Systems Integration, 2021, 3 (4) : 287 - 289
  • [4] Human-Autonomy Teaming
    How, Jonathan P.
    IEEE CONTROL SYSTEMS MAGAZINE, 2016, 36 (02): : 3 - 4
  • [5] Towards Neuro-Symbolic AI for Assured and Trustworthy Human-Autonomy Teaming
    Rawat, Danda B.
    2023 5TH IEEE INTERNATIONAL CONFERENCE ON TRUST, PRIVACY AND SECURITY IN INTELLIGENT SYSTEMS AND APPLICATIONS, TPS-ISA, 2023, : 177 - 179
  • [6] Why Human-Autonomy Teaming?
    Shively, R. Jay
    Lachter, Joel
    Brandt, Summer L.
    Matessa, Michael
    Battiste, Vernol
    Johnson, Walter W.
    ADVANCES IN NEUROERGONOMICS AND COGNITIVE ENGINEERING (AHFE 2017), 2018, 586 : 3 - 11
  • [7] Towards Neuro-Symbolic Reinforcement Learning For Trustworthy Human-Autonomy Teaming
    Gurung, Priti
    Li, Jiang
    Rawat, Danda B.
    ASSURANCE AND SECURITY FOR AI-ENABLED SYSTEMS, 2024, 13054
  • [8] The Evaluation of a Playbook Interface for Human-Autonomy Teaming in Single Pilot Operations
    Tokadli, Guliz
    Dorneich, Michael C.
    Matessa, Michael
    Eda, Seiya
    2019 IEEE/AIAA 38TH DIGITAL AVIONICS SYSTEMS CONFERENCE (DASC), 2019,
  • [9] Teaming With a Synthetic Teammate: Insights into Human-Autonomy Teaming
    McNeese, Nathan J.
    Demir, Mustafa
    Cooke, Nancy J.
    Myers, Christopher
    HUMAN FACTORS, 2018, 60 (02) : 262 - 273
  • [10] Human-Autonomy Teaming and Agent Transparency
    Chen, Jessie Y. C.
    Selkowitz, Anthony R.
    Stowers, Kimberly
    Lakhmani, Shan G.
    Barnes, Michael J.
    COMPANION OF THE 2017 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'17), 2017, : 91 - 92