Fairness in job recommendations: estimating, explaining, and reducing gender gaps. - Guillaume Bied; Christophe Gaillac; Morgane Hoffmann; Philippe Caillou; Bruno Crépon; Solal Nathan; Michèle Sebag (AEQUITAS@ECAI, 2023)

RECTO : REcommandation diminuant la Congestion par Transport Optimal. - Guillaume Bied; Elia Perennes; Solal Nathan; Victor Naya; Philippe Caillou; Bruno Crépon; Christophe Gaillac; Michèle Sebag (APIA, 2023)

Toward Job Recommendation for All. - Guillaume Bied; Solal Nathan; Elia Perennes; Morgane Hoffmann; Philippe Caillou; Bruno Crépon; Christophe Gaillac; Michèle Sebag (IJCAI, 2023)

Designing Recommender Systems for the Labor Market. (Concevoir et évaluer les algorithmes de recommandation pour le marché du travail). - Guillaume Bied (2024)

InfoClus: Informative Clustering of High-Dimensional Data Embeddings. - Fuyin Lai; Edith Heiter; Guillaume Bied; Jefrey Lijffijt (ECML/PKDD (1), 2025)

What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices. - Sander Noels; Guillaume Bied; Maarten Buyl; Alexander Rogiers; Yousra Fettach; Jefrey Lijffijt; Tijl De Bie (ECML/PKDD (1), 2025)

Fifth Workshop on Recommender Systems for Human Resources (RecSys in HR 2025). - Toine Bogers; Mesut Kaya; Jens-Joris Decorte; Chris Johnson 011; Guillaume Bied (RecSys, 2025)

BiMi Sheets: Infosheets for bias mitigation methods. - MaryBeth Defrance; Guillaume Bied; Maarten Buyl; Jefrey Lijffijt; Tijl De Bie (CoRR, 2025)

Building and Measuring Trust between Large Language Models. - Maarten Buyl; Yousra Fettach; Guillaume Bied; Tijl De Bie (CoRR, 2025)