How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law

no code implementations • 19 Apr 2024 • Benjamin Fresz, Elena Dubovitskaya, Danilo Brajovic, Marco Huber, Christian Horz

This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI).

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

DMLR: Data-centric Machine Learning Research -- Past, Present and Future

no code implementations • 21 Nov 2023 • Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William A Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson

Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science.

Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development

no code implementations • 21 Jul 2023 • Danilo Brajovic, Niclas Renner, Vincent Philipp Goebels, Philipp Wagner, Benjamin Fresz, Martin Biller, Mara Klaeb, Janika Kutz, Jens Neuhuettler, Marco F. Huber

In this work we merge recent regulation efforts by the European Union and first proposals for AI guidelines with recent trends in research: data and model cards.

Document AI

A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules

no code implementations • 23 Aug 2022 • Paul-Amaury Matt, Rosina Ziegler, Danilo Brajovic, Marco Roth, Marco F. Huber

Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set.

Combinatorial Optimization

Towards Understanding Data Values: Empirical Results on Synthetic Data

no code implementations • 29 Sep 2021 • Danilo Brajovic, Omar de Mitri, Alex Windberger, Marco Huber

Understanding the influence of data on machine learning models is an emerging research field.

Data Valuation