DIANNA

• Provides an easy-to-use interface for non (X)AI experts
• Implements well-known XAI methods (LIME, RISE and Kernal SHAP) chosen by systematic and objective evaluation criteria
• Supports the de-facto standard of neural network models - ONNX
• Supports images, text, time series, and tabular data modalities, embeddings are currently being developed
• Comes with simple intuitive image, text, time series, and tabular benchmarks, so can help you with your XAI research
• Scientific use-cases tutorials
• Easily extendable to other XAI methods

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.) concerning 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 library supports the best XAI methods in the context of scientific usage providing their OSS implementation based on the ONNX standard and demonstrations on benchmark datasets. DIANNA supports images, text, time-series, and tabular data, while embeddings support is currently being worked on.