Research Overview

Our team conducts research at the intersection of Data Science and Social Sciences with a focus on topics such as explainable & fair AI, AI robustness, AI auditing, AI alignment and safety in general.

The main focus topics of our research are:

  • Explainable AI and Interpretability: Research focusing on the explainability of AI models, offering local explanations for predictions, algorithmic recourse, and user-guided explainability.
  • Ethics and Fairness in AI: Studies discussing ethical implications of AI, with emphasis on balancing actionable explanations and the right to be forgotten, and ensuring fairness in accordance with European values.
  • Performance Evaluation, Efficiency, and Accountability of AI Models: Research discussing efficient strategies for evaluating and auditing AI models, especially those for evolving data streams, attribution methods, and data recovered from embedding spaces (through generative approaches).

Open Positions

March 2024: We are hiring for a PhD Position in Artificial Intelligence for Data GenerationPlease follow the link for additional details.

News

  • February 2024: Our paper "Crowdsourcing is breaking my bank! A self-supervised Method for Fine-tuning Pre-trained Language models using Proximal Policy Optimization" by Shuo Yang and Gjergji Kasneci has been accepted at LREC-COLING 2024. A preprint is available on arXiv.
  • January 2024: Our work "I Prefer Not to Say: Protecting User Consent in Models with Optional Personal Data" by Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, and Gjergji Kasneci was accepted as an Oral Presentation at AAAI-2024. A preprint is available on arXiv.
  • September 2023: We are happy to announce that our paper "Gaussian Membership Inference Privacy" by Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci has been accepted for publication at NeurIPS 2023. A preprint is available on arXiv.