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DIANNA - Deep Insight and Neural Networks Analysis

Explainable AI tool for scientists

Modern scientific challenges are often tackled with (Deep) Neural Networks (DNN). Despite their high predictive accuracy, DNNs lack inherent explainability. Many scientists do not harvest DNNs power because of lack of trust and understanding of their working. Meanwhile, the eXplainable AI (XAI) research offers some post-hoc (after training) interpretability methods that provide insight into the DNN reasoning by quantifying the relevance of individual features (image pixels, words in text, etc.) with respect to the prediction. These relevance heatmaps indicate how the network has reached its decision directly in the input modality (images, text, speech etc.) of the scientific data. Representing visually the captured knowledge by the AI system can become a source of scientific insights. There are many Open Source Software (OSS) implementations of these methods, alas, supporting a single DNN format, while standards like Open Neural Network eXchange (ONNX) exist. The libraries are known mostly by the AI experts. For the adoption by the wide scientific community understanding of the XAI methods and well-documented and standardized OSS are needed. The DIANNA project aims at determining the best XAI methods in the context of scientific usage providing their OSS implementation based on the ONNX standard and demonstrations on benchmark datasets for images, text and time-series.

Participating organisations

Netherlands eScience Center
SURF

Output

How to find your Artificial Intelligence explainer

Author(s): Elena Ranguelova
Published in 2022

Team

GC
Giulia Crocioni
developer dashboard
Netherlands eScience Center
PC
Pranav Chandramouli
LO
Laura Ootes
developer dashboard
Netherlands eScience Center
Christiaan Meijer
Christiaan Meijer
eScience Research Engineer
Netherlands eScience Center
Elena Ranguelova
Elena Ranguelova
Principal investigator
Netherlands eScience Center
Leon Oostrum
Leon Oostrum
eScience Research Engineer
Netherlands eScience Center
Patrick Bos
Patrick Bos
eScience Research Engineer
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center
YL
Yang Liu
eScience Research Engineer
Netherlands eScience Center

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DIANNA

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Deep Insight And Neural Network Analysis, DIANNA is the only Explainable AI, XAI library for scientists supporting Open Neural Network Exchange, ONNX - the de facto standard models format.

Updated 3 weeks ago
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