Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score

20 May 2022

Papers citing "Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score"

3 / 3 papers shown

Title
Evaluating the Interpretability of Generative Models by Interactive Reconstruction A. Ross Nina Chen Elisa Zhao Hang Elena L. Glassman Finale Doshi-Velez 105 49 0 02 Feb 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,090 0 24 Oct 2016