Explainable AI: introducing trust and comprehensibility to AI engineering. - Nadia Burkart; Danilo Brajovic; Marco F. Huber (Autom., 2022)

A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules. - Paul-Amaury Matt; Rosina Ziegler; Danilo Brajovic; Marco Roth; Marco F. Huber (CoRR, 2022)

Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development. - Danilo Brajovic; Niclas Renner; Vincent Philipp Goebels; Philipp Wagner 005; Benjamin Frész; Martin Biller; Mara Klaeb; Janika Kutz; Jens Neuhüttler; Marco F. Huber (CoRR, 2023)

DMLR: Data-centric Machine Learning Research - Past, Present and Future. - Luis Oala; Manil Maskey; Lilith Bat-Leah; Alicia Parrish; Nezihe Merve Gürel; Tzu-Sheng Kuo; Yang Liu 018; Rotem Dror; Danilo Brajovic; Xiaozhe Yao; Max Bartolo; William Gaviria Rojas; et al. (CoRR, 2023)

Merging Explainable AI into Automotive Software Development. - Danilo Brajovic; Marco F. Huber (ERCIM News, 2023)

How Should AI Decisions Be Explained? Requirements for Explanations from the Perspective of European Law. - Benjamin Frész; Elena Dubovitskaya; Danilo Brajovic; Marco F. Huber; Christian Horz (AIES (1), 2024)

AI Certification: Empirical Investigations into Possible Cul-De-Sacs and Ways Forward (Practitioner Track). - Benjamin Frész; Danilo Brajovic; Marco F. Huber (SAIA, 2024)

The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis. - Benjamin Frész; Vincent Philipp Goebels; Safa Omri; Danilo Brajovic; Andreas Aichele; Janika Kutz; Jens Neuhüttler; Marco F. Huber (CoRR, 2024)