Asset Type
Docker container
Fraunhofer uncertainty metrics for classification tasks
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
Verifiable AI
The Verifiable AI objectives are organized in four open research questions that constitute four dimensions of the grand challenge resulting from the emergent use of AI in safety-critical applications. These four dimensions also represent the natural way to organize the background material on Verifiable AI:
- Dependability with AI: how to design and verify dependable and secure systems that include unverifiable AI components?
- Dependability of AI: how to verify the dependability and security of AI components themselves (i.e., domain-independent inference engines, as well as knowledge bases either machine learned from data or manually encoded by human domain experts)?
- AI for dependability: which AI techniques can be themselves leveraged to automate the design and verification of systems that include unverified AI components?
- Meta-AI dependability: which AI techniques can be themselves leveraged to automate the design and verification of AI components (meta-AI)?
Video file
Asset Type
Docker container
VERIFAI
Verifiable and Explainable RIsk Forecasting Artificial Intelligence Framework
Asset Type
Docker container
Clingo Answer Set Solver
State-of-the-art solver for logic programming under the answer set semantics.
Asset Type
Executable
LegAi Editor
Current solutions to legal translation are based on machine learning techniques. Such techniques are based on statistics and always exhibit a margin of error. In addition, machine learning, which is based on data and algorithms, is by definition unethical...
Asset Type
Dataset
Vibration sensor measurements in a robotic pump
A data set of 380 million measurements from a hydraulic pump that can be mounted on an industrial robot, for example, to pump liquid paint for spray painting. The data set spans two months in 1-second intervals.
Asset Type
Jupyter Notebook
Fairness-Aware ML Tutorial Series
This tutorial series offers a hands-on guide to fairness-aware machine learning that targets beginners in Fair-ML.
Asset Type
Dataset
Vibration sensor measurements in a robotic wrist
A data set of 1.8 billion measurements from a mechanical wrist with three axes that can hold tools, for example, for spray painting in combination with a pump. The data set spans six months in 1-second intervals.
Asset Type
Jupyter Notebook
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees
A novel progressive training algorithm for learning Graph Neural Networks with Differential Privacy guarantees