Scalable Trustworthy AI

Creating scalable and trustworthy AI with human guidance

Overview

Artificial intelligence (AI) bears hope for a positive future for humanity. For example, AI could help us fight climate change by optimising power usage. Like fire and electricity have fundamentally changed the life of mankind (quote), AI may also increase overall human productivity to balance out the ageing population and increase humanity’s overall welfare.

The status quo is that AI is far from being perfect. One of the greatest issues is that AI systems are not trustworthy yet. It is difficult to understand when and why they fail. This is especially so when the models are deployed in environments that are different from the training environment (even slightly). Even worse, naive or malicious application of AI systems causes harm to humanity by amplifying political polarisation, by treating minority groups unfairly, and by jeopardising the human liberty and free will.

This leads to our study of Trustworthy AI. We aim to understand the trustworthiness of current AI systems and develop new technologies that enhance their trustworthiness. We focus on three sub-topics among other important topics:

Fortunately, we are not alone in this effort. There are many other research labs around the world that make important contributions on Trustworthy AI. Our group find our uniqueness by striving for working solutions that are widely applicable and can be deployed at a large-scale. We thus name our group Scalable Trustworthy AI. To achieve the scalability, we commit ourselves to the following principles:

With these principles in mind, we do research on Scalable Trustworthy AI technologies to guide the field to the right direction. We hope to contribute to mitigating the negative side-effects of AI and accelerating the AI-led advances for the future of humanity.

STAI group is part of the Tübingen AI Center and the University of Tübingen. STAI is also within the ecosystem of International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the ELLIS Society.

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Members

Seong Joon Oh

Group Leader

Elisa Nguyen

PhD Student

Publications

ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO

ECCV

Dataset Condensation via Efficient Synthetic-Data Parameterization

ICML

Weakly Supervised Semantic Segmentation Using Out-of-Distribution Data

CVPR

Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective

ICLR

Openings

PhD Students

We are looking for PhD candidates. The students are expected to build their expertise on sub-topics of Scalable Trustworthy AI, including explainability, robustness, and uncertainty. They are expected to contribute to the research community through conference papers. Starting date is flexible and PhD students will work at the Tübingen AI Center. Payment and benefits are based on the TVöD guidelines. It is generally recommended that the candidates apply through the International Max Planck Research School for Intelligent Systems (IMPRS), whose deadline is usually in November. However, candidates may contact Seong Joon Oh with their CVs to already discuss the fit.