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Maximilian Poretschkin

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affiliation: Fraunhofer IAIS, Sankt Augustin, Germany
affiliation: University of Bonn, Germany

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2020 – today

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2025
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  - conf/saia/2024
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Rebekka Görge, Elena Haedecke, Maximilian Poretschkin, Anna Schmitz:
Symposium on Scaling AI Assessments, SAIA 2024, September 30 to October 1, 2024, Cologne, Germany. OASIcs 126, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2025, ISBN 978-3-95977-357-7 [contents]
2024
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  - journals/aiethics/BraunschweigBCHKMP24
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Bertrand Braunschweig, Stefan Buijsman, Faïcel Chamroukhi, Fredrik Heintz, Foutse Khomh, Juliette Mattioli, Maximilian Poretschkin:
AITA: AI trustworthiness assessment. AI Ethics 4(1): 1-3 (2024)
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Sergio Genovesi, Julia Maria Mönig, Anna Schmitz, Maximilian Poretschkin, Maram Akila, Manoj Kahdan, Romina Kleiner, Lena Krieger, Alexander Zimmermann:
Standardizing fairness-evaluation procedures: interdisciplinary insights on machine learning algorithms in creditworthiness assessments for small personal loans. AI Ethics 4(2): 537-553 (2024)
[c1]
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  - conf/aies/MuellerGBPP24
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Felix B. Mueller, Rebekka Görge, Anna K. Bernzen, Janna C. Pirk, Maximilian Poretschkin:
LLMs and Memorization: On Quality and Specificity of Copyright Compliance. AIES (1) 2024: 984-996
[i5]
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  - journals/corr/abs-2405-04937
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Michael Mock, Sebastian Schmidt, Felix Müller, Rebekka Görge, Anna Schmitz, Elena Haedecke, Angelika Voss, Dirk Hecker, Maximilian Poretschkin:
Developing trustworthy AI applications with foundation models. CoRR abs/2405.04937 (2024)
[i4]
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  - journals/corr/abs-2405-18492
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Felix Müller, Rebekka Görge, Anna K. Bernzen, Janna C. Pirk, Maximilian Poretschkin:
LLMs and Memorization: On Quality and Specificity of Copyright Compliance. CoRR abs/2405.18492 (2024)
2023
[i3]
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  - journals/corr/abs-2307-03681
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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:
Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog. CoRR abs/2307.03681 (2023)
2022
[j1]
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  - journals/at/SchmitzAHPW22
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Anna Schmitz, Maram Akila, Dirk Hecker, Maximilian Poretschkin, Stefan Wrobel:
The why and how of trustworthy AI. Autom. 70(9): 793-804 (2022)
[i2]
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  - journals/corr/abs-2205-01040
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Maximilian Pintz, Joachim Sicking, Maximilian Poretschkin, Maram Akila:
A Survey on Uncertainty Toolkits for Deep Learning. CoRR abs/2205.01040 (2022)
2021
[i1]
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  - journals/corr/abs-2104-14235
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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:
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. CoRR abs/2104.14235 (2021)

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