---
_id: '56282'
abstract:
- lang: eng
  text: "Algorithmic bias has long been recognized as a key problem affecting decision-making
    processes that integrate artificial intelligence (AI) technologies. The increased
    use of AI in making military decisions relevant to the use of force has sustained
    such questions about biases in these technologies and in how human users programme
    with and rely on data based on hierarchized socio-cultural norms, knowledges,
    and modes of attention.\r\n\r\nIn this post, Dr Ingvild Bode, Professor at the
    Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD
    researcher at the “Meaningful Human Control: Between Regulation and Reflexion”
    project, Paderborn University, unpack the problem of algorithmic bias with reference
    to AI-based decision support systems (AI DSS). They examine three categories of
    algorithmic bias – preexisting bias, technical bias, and emergent bias – across
    four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing
    discussion about AI in the military domain should consider the impact of algorithmic
    bias on AI DSS more seriously."
author:
- first_name: Ishmael
  full_name: Bhila, Ishmael
  id: '105772'
  last_name: Bhila
- first_name: Ingvild
  full_name: Bode, Ingvild
  last_name: Bode
citation:
  ama: Bhila I, Bode I. <i>The Problem of Algorithmic Bias in AI-Based Military Decision
    Support Systems</i>. ICRC Humanitarian Law &#38; Policy Blog; 2024.
  apa: Bhila, I., &#38; Bode, I. (2024). <i>The problem of algorithmic bias in AI-based
    military decision support systems</i>. ICRC Humanitarian Law &#38; Policy Blog.
  bibtex: '@book{Bhila_Bode_2024, title={The problem of algorithmic bias in AI-based
    military decision support systems}, publisher={ICRC Humanitarian Law &#38; Policy
    Blog}, author={Bhila, Ishmael and Bode, Ingvild}, year={2024} }'
  chicago: Bhila, Ishmael, and Ingvild Bode. <i>The Problem of Algorithmic Bias in
    AI-Based Military Decision Support Systems</i>. ICRC Humanitarian Law &#38; Policy
    Blog, 2024.
  ieee: I. Bhila and I. Bode, <i>The problem of algorithmic bias in AI-based military
    decision support systems</i>. ICRC Humanitarian Law &#38; Policy Blog, 2024.
  mla: Bhila, Ishmael, and Ingvild Bode. <i>The Problem of Algorithmic Bias in AI-Based
    Military Decision Support Systems</i>. ICRC Humanitarian Law &#38; Policy Blog,
    2024.
  short: I. Bhila, I. Bode, The Problem of Algorithmic Bias in AI-Based Military Decision
    Support Systems, ICRC Humanitarian Law &#38; Policy Blog, 2024.
date_created: 2024-09-30T11:44:28Z
date_updated: 2024-11-26T09:49:48Z
has_accepted_license: '1'
keyword:
- Algorithmic Bias
- AI
- Decision Support Systems
- Autonomous Weapons Systems
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://blogs.icrc.org/law-and-policy/2024/09/03/the-problem-of-algorithmic-bias-in-ai-based-military-decision-support-systems/
oa: '1'
publication_status: published
publisher: ICRC Humanitarian Law & Policy Blog
related_material:
  link:
  - relation: confirmation
    url: https://blogs.icrc.org/law-and-policy/2024/09/03/the-problem-of-algorithmic-bias-in-ai-based-military-decision-support-systems/
status: public
title: The problem of algorithmic bias in AI-based military decision support systems
type: misc
user_id: '105772'
year: '2024'
...
---
_id: '29539'
abstract:
- lang: eng
  text: Explainable Artificial Intelligence (XAI) is currently an important topic
    for the application of Machine Learning (ML) in high-stakes decision scenarios.
    Related research focuses on evaluating ML algorithms in terms of interpretability.
    However, providing a human understandable explanation of an intelligent system
    does not only relate to the used ML algorithm. The data and features used also
    have a considerable impact on interpretability. In this paper, we develop a taxonomy
    for describing XAI systems based on aspects about the algorithm and data. The
    proposed taxonomy gives researchers and practitioners opportunities to describe
    and evaluate current XAI systems with respect to interpretability and guides the
    future development of this class of systems.
author:
- first_name: Jan-Peter
  full_name: Kucklick, Jan-Peter
  id: '77066'
  last_name: Kucklick
citation:
  ama: 'Kucklick J-P. Towards a model- and data-focused taxonomy of XAI systems. In:
    <i>Wirtschaftsinformatik 2022 Proceedings</i>. ; 2022.'
  apa: Kucklick, J.-P. (2022). Towards a model- and data-focused taxonomy of XAI systems.
    <i>Wirtschaftsinformatik 2022 Proceedings</i>. Wirtschaftsinformatik 2022 (WI22),
    Nürnberg (online).
  bibtex: '@inproceedings{Kucklick_2022, title={Towards a model- and data-focused
    taxonomy of XAI systems}, booktitle={Wirtschaftsinformatik 2022 Proceedings},
    author={Kucklick, Jan-Peter}, year={2022} }'
  chicago: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI
    Systems.” In <i>Wirtschaftsinformatik 2022 Proceedings</i>, 2022.
  ieee: J.-P. Kucklick, “Towards a model- and data-focused taxonomy of XAI systems,”
    presented at the Wirtschaftsinformatik 2022 (WI22), Nürnberg (online), 2022.
  mla: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI Systems.”
    <i>Wirtschaftsinformatik 2022 Proceedings</i>, 2022.
  short: 'J.-P. Kucklick, in: Wirtschaftsinformatik 2022 Proceedings, 2022.'
conference:
  end_date: 2022-02-23
  location: Nürnberg (online)
  name: Wirtschaftsinformatik 2022 (WI22)
  start_date: 2022-02-21
date_created: 2022-01-26T08:22:03Z
date_updated: 2022-01-26T08:24:30Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence
- XAI
- Interpretability
- Decision Support Systems
- Taxonomy
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1056&context=wi2022
oa: '1'
publication: Wirtschaftsinformatik 2022 Proceedings
status: public
title: Towards a model- and data-focused taxonomy of XAI systems
type: conference
user_id: '77066'
year: '2022'
...