In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

8 May 2020

Papers citing "In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction"

9 / 9 papers shown

Title
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning Caleb J. S. Barr Olivia Erdelyi Paul D. Docherty Randolph C. Grace FaML 63 0 0 10 Nov 2024
Probabilistic Scoring Lists for Interpretable Machine Learning Jonas Hanselle Stefan Heid Zhigang Zeng Eyke Hüllermeier 20 0 0 31 Jul 2024
Amazing Things Come From Having Many Good Models Cynthia Rudin Chudi Zhong Lesia Semenova Margo Seltzer Ronald E. Parr Jiachang Liu Srikar Katta Jon Donnelly Harry Chen Zachery Boner 21 23 0 05 Jul 2024
Feature Importance Measurement based on Decision Tree Sampling Chao Huang Diptesh Das Koji Tsuda FAtt 16 2 0 25 Jul 2023
Fair Spatial Indexing: A paradigm for Group Spatial Fairness Sina shaham Gabriel Ghinita Cyrus Shahabi 12 0 0 05 Feb 2023
FasterRisk: Fast and Accurate Interpretable Risk Scores Jiachang Liu Chudi Zhong Boxuan Li Margo Seltzer Cynthia Rudin 28 16 0 12 Oct 2022
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values Zijie J. Wang Alex Kale Harsha Nori P. Stella M. Nunnally Duen Horng Chau Mihaela Vorvoreanu J. W. Vaughan R. Caruana KELM 46 27 0 30 Jun 2022
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 43 196 0 06 Apr 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016