---
_id: '65063'
abstract:
- lang: eng
  text: "<jats:title>Abstract</jats:title>\r\n                  <jats:p>\r\n                    This
    chapter critically examines how social explainable AI (sXAI) can better support
    AI practitioners in ensuring fairness in AI-based decision-making. We argue for
    a fundamental shift: Fairness should be understood not as a technical property
    or an information problem, but as a matter of vulnerability—focusing on the real-world
    impacts of AI on individuals and groups, especially those most at risk. Hereby,
    we call for a shift in perspective: from fair AI to\r\n                    <jats:italic>tasking
    AI fairly</jats:italic>\r\n                    . To motivate our vulnerability
    approach, we review the “Dutch welfare fraud scandal” (system risk indication—SyRI)
    and current challenges in the field of fair AI/machine learning (ML). Vulnerability
    of a person or members of a definable group of persons is a complex relational
    notion, and not a technical property of a technical system. Accordingly, we suggest
    several nontechnical strategies that hold the promise to compensate for the insufficiency
    of purely technical approaches to fairness and other ethical issues in the practical
    use of AI-based systems. To discuss how sXAI, due to its interactive and adaptive
    social character, might better fulfill this role than current XAI techniques,
    we provide a toy scenario for how sXAI might support the virtuous AI practitioner
    in an ethical inquiry. Finally, we also address challenges and limits of our approach.\r\n
    \                 </jats:p>"
author:
- first_name: Suzana
  full_name: Alpsancar, Suzana
  id: '93637'
  last_name: Alpsancar
- first_name: Eugenia
  full_name: Stamboliev, Eugenia
  last_name: Stamboliev
citation:
  ama: 'Alpsancar S, Stamboliev E. Tasking AI Fairly. How to Empower AI Practitioners
    With sXAI? In: <i>Social Explainable AI</i>. Springer Nature Singapore; 2026:557-581.
    doi:<a href="https://doi.org/10.1007/978-981-96-5290-7_29">10.1007/978-981-96-5290-7_29</a>'
  apa: Alpsancar, S., &#38; Stamboliev, E. (2026). Tasking AI Fairly. How to Empower
    AI Practitioners With sXAI? In <i>Social Explainable AI</i> (pp. 557–581). Springer
    Nature Singapore. <a href="https://doi.org/10.1007/978-981-96-5290-7_29">https://doi.org/10.1007/978-981-96-5290-7_29</a>
  bibtex: '@inbook{Alpsancar_Stamboliev_2026, place={Singapore}, title={Tasking AI
    Fairly. How to Empower AI Practitioners With sXAI?}, DOI={<a href="https://doi.org/10.1007/978-981-96-5290-7_29">10.1007/978-981-96-5290-7_29</a>},
    booktitle={Social Explainable AI}, publisher={Springer Nature Singapore}, author={Alpsancar,
    Suzana and Stamboliev, Eugenia}, year={2026}, pages={557–581} }'
  chicago: 'Alpsancar, Suzana, and Eugenia Stamboliev. “Tasking AI Fairly. How to
    Empower AI Practitioners With SXAI?” In <i>Social Explainable AI</i>, 557–81.
    Singapore: Springer Nature Singapore, 2026. <a href="https://doi.org/10.1007/978-981-96-5290-7_29">https://doi.org/10.1007/978-981-96-5290-7_29</a>.'
  ieee: 'S. Alpsancar and E. Stamboliev, “Tasking AI Fairly. How to Empower AI Practitioners
    With sXAI?,” in <i>Social Explainable AI</i>, Singapore: Springer Nature Singapore,
    2026, pp. 557–581.'
  mla: Alpsancar, Suzana, and Eugenia Stamboliev. “Tasking AI Fairly. How to Empower
    AI Practitioners With SXAI?” <i>Social Explainable AI</i>, Springer Nature Singapore,
    2026, pp. 557–81, doi:<a href="https://doi.org/10.1007/978-981-96-5290-7_29">10.1007/978-981-96-5290-7_29</a>.
  short: 'S. Alpsancar, E. Stamboliev, in: Social Explainable AI, Springer Nature
    Singapore, Singapore, 2026, pp. 557–581.'
date_created: 2026-03-19T11:03:30Z
date_updated: 2026-03-19T11:53:42Z
department:
- _id: '26'
- _id: '756'
doi: 10.1007/978-981-96-5290-7_29
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1007/978-981-96-5290-7_29
oa: '1'
page: 557-581
place: Singapore
project:
- _id: '370'
  name: 'TRR 318; TP B06: Ethik und Normativität der erklärbaren KI'
publication: Social Explainable AI
publication_identifier:
  isbn:
  - '9789819652891'
  - '9789819652907'
publication_status: published
publisher: Springer Nature Singapore
status: public
title: Tasking AI Fairly. How to Empower AI Practitioners With sXAI?
type: book_chapter
user_id: '93637'
year: '2026'
...