Alibi aims to be the go-to library for ML model interpretability. There are multiple challenges for developing a high quality, production-ready library that achieves this. In addition to having high quality reference implementations of the most promising algorithms, we need extensive documentation and case studies comparing the different interpretability methods and their respective pros and cons. A clean and a usable API is also a priority.

Short term

  • Complete refactoring to enable multiple backends (TensorFlow, PyTorch) and distributed computing

  • AnchorText improvements using generative models

Medium term

  • PyTorch support for white-box gradient based explanations

  • Improve black-box counterfactual explanations using gradient-free methods

Long term

  • Ongoing optimizations of existing algorithms (speed, parallelisation, explanation quality)

  • Explanations for sequential and structured data