---
res:
  bibo_abstract:
  - "<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>@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Suzana
      foaf_name: Alpsancar, Suzana
      foaf_surname: Alpsancar
      foaf_workInfoHomepage: http://www.librecat.org/personId=93637
  - foaf_Person:
      foaf_givenName: Eugenia
      foaf_name: Stamboliev, Eugenia
      foaf_surname: Stamboliev
  bibo_doi: 10.1007/978-981-96-5290-7_29
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/9789819652891
  - http://id.crossref.org/issn/9789819652907
  dct_language: eng
  dct_publisher: Springer Nature Singapore@
  dct_title: Tasking AI Fairly. How to Empower AI Practitioners With sXAI?@
...