Fair-by-design explainable models for prediction of recidivism

18 September 2019

Papers citing "Fair-by-design explainable models for prediction of recidivism"

8 / 8 papers shown

Title
Learning to Advise Humans in High-Stakes Settings Nicholas Wolczynski M. Saar-Tsechansky Tong Wang 53 0 0 23 Oct 2022
Explainable Global Fairness Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi 79 3 0 27 Sep 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aïvodji Stephen H. Bach Himabindu Lakkaraju 92 58 0 15 May 2022
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data Raphael Mazzine David Martens 100 33 0 09 Jul 2021
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 105 88 0 08 May 2020
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference Plamen Angelov Eduardo Soares 44 14 0 02 Feb 2020
A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced Classification Xiaowei Gu Plamen Angelov Eduardo Soares 49 66 0 25 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 182 6,380 0 22 Oct 2019