Biais algorithmique

Applied sciences
Information engineering
Intelligence artificielle
Intelligence artificielle g...

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (31)

A Statistical Test for Probabilistic Fairness

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of data, ...

2021

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Mirko Marras

This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing ...

IEEE COMPUTER SOC2021

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

2021

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

The Imitation Game: Algorithm Selection by Exploiting Black-Box Recommenders

Rachid Guerraoui, Georgios Damaskinos

Cross-validation is commonly used to select the recommendation algorithms that will generalize best on yet unknown data. Yet, in many situations the available dataset used for cross-validation is scarce and the selected algorithm might not be the best suit ...

Springer2021

Online Dating Quantification Practices: A Human-Machine Learning Process

Jessica Carolina Pidoux

The phenomenon of online dating via web and mobile phone applications involves several actors: graphical interfaces, developers, algorithmic systems for user matching, and users. These actors have been studied in parallel by the social sciences and by comp ...

EPFL2021

Model-based 3D Face Reconstruction

Christophe René Joseph Ecabert

The human face plays an essential role in social interactions as it brings information about someone's identity, state of mind, or mood. People are, by nature, very good at catching this non-spoken information. Therefore, scientists have been interested in ...

EPFL2021

Science Communication Desperately Needs More Aligned Recommendation Algorithms

Le Nguyen Hoang

With 1 billion watch-time hours per day, YouTube now plays a major role in communication. Unfortunately, a large amount of misinformation is produced and widely shared on this platform (Donzelli et al., 2018; Allgaier, 2019; Loeb et al., 2019). In this pap ...

FRONTIERS MEDIA SA2020

A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples

Pierre Dillenbourg, Jennifer Kaitlyn Olsen, Louis Pierre Faucon

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. I ...

ASSOC COMPUTING MACHINERY2020

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Anastasia Ailamaki, Periklis Chrysogelos, Panagiotis Sioulas

In order to improve their power efficiency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the nece ...

2019

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture

Immanuel Chee Beng Koh

The spatial and formal conception of architecture, and thus its modes of design perception and representation, directly contributes to its machine-learnability; and consequently, its capacity in leveraging today's machine learning apparatus for design inno ...

EPFL2019

Page 2 sur 2