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  4. Shifting Paradigms: Value Sensitive Design for Fair AI Recruitment
 

Shifting Paradigms: Value Sensitive Design for Fair AI Recruitment

URI
https://arbor.bfh.ch/handle/arbor/46460
Version
Published
Date Issued
2025-03-20
Author(s)
Puttick, Alexandre Riemann  
Rigotti, Carlotta
Abouzeid, Ahmed
Fosch-Villaronga, Eduard
Kurpicz-Briki, Mascha  
Øztürk, Pinar
Type
Conference Paper
Language
English
Subjects

AI

fairness

value sensitive desig...

recruitment

diversity bias

Abstract
In this position paper, we advocate for the use of value sensitive design (VSD) as a framework for developing fair AI recruitment tools. As a starting point, we assert that the current paradigm in AI fairness in the hiring context is severely limiting. We then document an ongoing process within the EU-horizon project BIAS, seeking to escape this paradigm by applying VSD to the development of AI applications for candidate selection with diversity and fairness as focal points. In particular, we present case-based reasoning as a case study in the intentional operationalization of stakeholder positions on fairness and detail how such an approach can be further expanded, drawing from the concept of agonistic machine learning. In this endeavor, we hope to contribute to the discourse on the ethical design and use of AI within the labor market and in general.
DOI
https://doi.org/10.24451/arbor.12796
Publisher DOI
10.5281/zenodo.15649867
Publisher URL
https://zenodo.org/records/15649867
Related URL
https://ceur-ws.org/
Organization
Institute for Data Applications and Security (IDAS)  
IDAS / Applied Machine Intelligence  
Technik und Informatik  
Conference
2nd Workshop on AI bias: Measurements, Mitigation, Explanation Strategies 2025 (AIMMES 2025): Proceedings
Submitter
Kurpicz-Briki, Mascha
Citation apa
Puttick, A. R., Rigotti, C., Abouzeid, A., Fosch-Villaronga, E., Kurpicz-Briki, M., & Øztürk, P. (2025). Shifting Paradigms: Value Sensitive Design for Fair AI Recruitment. 2nd Workshop on AI bias: Measurements, Mitigation, Explanation Strategies 2025 (AIMMES 2025): Proceedings. https://doi.org/10.24451/arbor.12796
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