Maximilian Poretschkin

According to our database, Maximilian Poretschkin authored at least 10 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A global scale comparison of risk aggregation in AI assessment frameworks.

Anna Schmitz, Michael Mock, Rebekka Görge, Armin B. Cremers, Maximilian Poretschkin

AI Ethics, April, 2025

2024

Standardizing fairness-evaluation procedures: interdisciplinary insights on machine learning algorithms in creditworthiness assessments for small personal loans.

Sergio Genovesi, Julia Maria Mönig, Anna Schmitz, Maximilian Poretschkin, Maram Akila, Manoj Kahdan, Romina Kleiner, Lena Krieger, Alexander Zimmermann

AI Ethics, May, 2024

AITA: AI trustworthiness assessment.

Bertrand Braunschweig, Stefan Buijsman, Faïcel Chamroukhi, Fredrik Heintz, Foutse Khomh, Juliette Mattioli, Maximilian Poretschkin

AI Ethics, February, 2024

LLMs and Memorization: On Quality and Specificity of Copyright Compliance.

Felix Müller, Rebekka Görge, Anna K. Bernzen, Janna C. Pirk, Maximilian Poretschkin

CoRR, 2024

Developing trustworthy AI applications with foundation models.

Michael Mock, Sebastian Schmidt, Felix Müller, Rebekka Görge, Anna Schmitz, Elena Haedecke, Angelika Voss, Dirk Hecker, Maximilian Poretschkin

CoRR, 2024

LLMs and Memorization: On Quality and Specificity of Copyright Compliance.

Felix B. Mueller, Rebekka Görge, Anna K. Bernzen, Janna C. Pirk, Maximilian Poretschkin

Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

2023

Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog.

Maximilian Poretschkin, Anna Schmitz, Maram Akila, Linara Adilova, Daniel Becker, Armin B. Cremers, Dirk Hecker, Sebastian Houben, Michael Mock, Julia Rosenzweig, Joachim Sicking, Elena Schulz, Angelika Voss, Stefan Wrobel

CoRR, 2023

2022

A Survey on Uncertainty Toolkits for Deep Learning.

Maximilian Pintz, Joachim Sicking, Maximilian Poretschkin, Maram Akila

CoRR, 2022

The why and how of trustworthy AI.

Anna Schmitz, Maram Akila, Dirk Hecker, Maximilian Poretschkin, Stefan Wrobel

Autom., 2022

2021

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety.

Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle

CoRR, 2021