What's new with Explainable AI in Julia? | Hill | JuliaCon 2024

What's new with Explainable AI in Julia? by Adrian Hill
PreTalx: pretalx.com/ju...
GitHub: github.com/Jul...
In machine learning, understanding the inner workings of black-box models is critical to ensuring their safety and trustworthiness. The field of Explainable AI (XAI) aims to provide practitioners with methods to gain insight into the decision-making processes of their models.
The Julia-XAI ecosystem provides such methods, with a focus on post-hoc, local input space explanations. Simply put, methods that try to answer the question *"What part of the input is responsible for the model's output?"*.
Since our first presentation of *ExplainableAI.jl* at JuliaCon 2022, the package has been expanded into the **Julia-XAI ecosystem**. This lightning talk will cover the latest additions and present new features:
*XAIBase.jl:* Core package that defines the Julia-XAI interface, allowing developers to quickly implement or prototype new methods without writing boilerplate code.
*VisionHeatmaps.jl and TextHeatmaps.jl:* Lightweight dependencies for visualizing explanations of vision and language models.
*RelevancePropagation.jl:* A new package for Layer-wise Relevance Propagation (LRP) and Concept Relevance Propagation (CRP) for use with Flux.jl models, supporting ResNets and Transformer architectures.
*New XAI methods in ExplainableAI.jl*