Project Details
Description
Continuing the work in our Grand Solutions project called 'Jobmatch', where we recommend relevant candidates to recruiters, we wish to generate recommendations for job seekers, but in line with the recently accepted EU AI Act, we want to focus on the fairness of the recommendations, both from a job
seeker perspective (does everyone get the
opportunities they deserve), but also from a company perspective (how can we mitigate the popularity bias
that occurs from jobs from popular employers like Novo for instance?). Balancing these different demands requires a multi-stakeholder approach to
fairness in job recommendation.
seeker perspective (does everyone get the
opportunities they deserve), but also from a company perspective (how can we mitigate the popularity bias
that occurs from jobs from popular employers like Novo for instance?). Balancing these different demands requires a multi-stakeholder approach to
fairness in job recommendation.
| Acronym | FAIRMATCH |
|---|---|
| Status | Active |
| Effective start/end date | 01/04/2024 → 31/03/2027 |
Collaborative partners
- IT University of Copenhagen
- Jobindex (lead)
Funding
- Innovation Fund Denmark: DKK1,242,000.00
Keywords
- Algorithmic hiring
- Job recommendation
- Fairness
- Recruitment
- HR tech
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems
Burke, R., Adomavicius, G., Bogers, T., Noia, T. D., Kowald, D., Neidhardt, J., Özgöbek, Ö., Pera, M. S., Tintarev, N. & Ziegler, J., Sept 2025, In: International Journal of Human-Computer Studies. 203, 103560Research output: Journal Article or Conference Article in Journal › Journal article › Research › peer-review
Open Access -
From Queries to Candidates: Exploring Search and Source Interaction Behavior of Recruiters
Bogers, T., Kaya, M. & Gäde, M., 29 Apr 2025, Proceedings of the 2025 ACM SIGIR Conference on Human Information Interaction and Retrieval. New York, USA : Association for Computing Machinery, p. 112-128 17 p.Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
Open Access -
Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
Kaya, M. & Bogers, T., 22 Sept 2025, RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems. New York, NY, USA: Association for Computing Machinery, p. 257-267 11 p.Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
Press/Media
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AI-rekruttering skal nedbryde fordomme – ikke fastholde dem
Bogers, T., Kickbusch, J. & Kaya, M.
25/06/2024
1 Media contribution
Press/Media: Press / Media