External Resource
Towards a Rigorous Science of Interpretable Machine Learning
Doshi-Velez & Kim (arXiv, 2017)
Position paper proposing definitions and a taxonomy for rigorously evaluating interpretability in machine learning.
Theses this informs
- Right to TransparencyDiscusses
- AI ValuesDiscusses