From Parity to Preference-based Notions of Fairness in Classification
Author:
Muhammad Bilal Zafar, Max Planck Institute for Software Systems, Max Planck Institute
Abstract:
Many notions of fairness in data-driven decision making are inspired by the concept of discrimination in social sciences and law, and focus on ensuring parity (equality) in treatment or outcomes for different social groups. In this paper, we propose preference-based notions of fairness with the goals of avoiding potential ‘reverse-discrimination’ and enabling high decision accuracy. We introduce tractable proxies to design convex boundary-based classifiers that satisfy these new notions of fairness and show on the ProPublica COMPAS dataset that these notions allow for greater decision accuracy than parity-based fairness.
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/
Видео From Parity to Preference-based Notions of Fairness in Classification канала KDD2017 video
Muhammad Bilal Zafar, Max Planck Institute for Software Systems, Max Planck Institute
Abstract:
Many notions of fairness in data-driven decision making are inspired by the concept of discrimination in social sciences and law, and focus on ensuring parity (equality) in treatment or outcomes for different social groups. In this paper, we propose preference-based notions of fairness with the goals of avoiding potential ‘reverse-discrimination’ and enabling high decision accuracy. We introduce tractable proxies to design convex boundary-based classifiers that satisfy these new notions of fairness and show on the ProPublica COMPAS dataset that these notions allow for greater decision accuracy than parity-based fairness.
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/
Видео From Parity to Preference-based Notions of Fairness in Classification канала KDD2017 video
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Designing AI at Scale to Power Everyday Life
Estimation of Recent Ancestral Origins of Individuals on a Large Scale
struc2vec: Learning Node Representations from Structural Identity
Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention
A Local Algorithm for StructurePreserving Graph Cut
Multi-Aspect Streaming Tensor Completion
Visualizing Deep Learning Activations for Improved Malaria Cell Classification
Planning Bike Lanes based on SharingBikes' Trajectories
Tripoles: A New Class of Relationships in Time Series Data
Incorporating Feedback into Tree-based Anomaly Detection
Industrial Machine Learning
Weisfeiler-Lehman Neural Machine for Link Prediction
Optimal Reserve Price for Online Ads Trading Based on Inventory Identification
Multi-Aspect Streaming Tensor Completion
Dispatch with Confidence: Integration of machine learning
Learning certifiably optimal rule lists for categorical data
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Multitask Learning using Task Clustering
The Co-Evolution Model for Social Network Evolving and Opinion Migration
Revisiting power-law distributions in spectra of real world networks
KDD Business Lunch