Puttick, Alexandre RiemannAlexandre RiemannPuttickRigotti, CarlottaCarlottaRigottiAbouzeid, AhmedAhmedAbouzeidFosch-Villaronga, EduardEduardFosch-VillarongaKurpicz-Briki, MaschaMaschaKurpicz-BrikiØztürk, PinarPinarØztürk2026-01-152026-01-152025-03-20https://doi.org/10.24451/arbor.1279610.5281/zenodo.15649867https://arbor.bfh.ch/handle/arbor/46460In 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.enAIfairnessvalue sensitive designrecruitmentdiversity biasconference_item