626 citations · 1.5k across the 21 of their papers we have counts for
9 papers · 1 filter
Efficient fair PCA for fair representation learning
Matthäus Kleindessner, Michele Donini, Chris Russell +1
We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic informa…
Multi-objective Asynchronous Successive Halving
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar +2
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models. However, in a plethora of real-…
Pairwise Fairness for Ordinal Regression
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar +2
We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that i…
Fair Bayesian Optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar +3
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML model…
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +2
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups.…
On Fairness, Diversity and Randomness in Algorithmic Decision Making
Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi +1
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the…
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems
Miguel Ferreira, Muhammad Bilal Zafar, Krishna P. Gummadi
Bringing transparency to black-box decision making systems (DMS) has been a topic of increasing research interest in recent years. Traditional active and passive approaches to make…
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +1
Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical dec…
Fairness Constraints: Mechanisms for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +1
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of d…