Using FALCES against bias in automated decisions by integrating fairness in dynamic model ensembles. - Nico Lässig; Sarah Oppold; Melanie Herschel (BTW, 2021)

The ArchIBALD Data Integration Platform: Bridging Fragmented Processes in the Building Industry. - Nico Lässig; Melanie Herschel; Alexander Reichle; Carsten Ellwein; Alexander Verl (CAiSE Forum, 2022)

Metrics and Algorithms for Locally Fair and Accurate Classifications using Ensembles. - Nico Lässig; Sarah Oppold; Melanie Herschel (Datenbank-Spektrum, 2022)

Towards an AutoML System for Fair Classifications∗. - Nico Lässig (ICDE, 2023)

Putting Co-Design-Supporting Data Lakes to the Test: An Evaluation on AEC Case Studies. - Melanie Herschel; Andreas Gienger; Anja Patricia Regina Lauer; Charlotte Stein; Lior Skoury; Nico Lässig; Carsten Ellwein; Alexander Verl; Thomas Wortmann; Cristina Tarín-Sauer (DaWaK, 2024)

FALCC: Efficiently performing locally fair and accurate classifications. - Nico Lässig; Melanie Herschel (EDBT, 2024)

FairCR - An Evaluation and Recommendation System for Fair Classification Algorithms. - Nico Lässig; Melanie Herschel; Ole Nies (ICDE, 2024)