Publications associées à Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Matthias Grossglauser, Aswin Suresh, Chi Hsuan Wu

We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

2024

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Andreas Fuster

We conduct two survey experiments to study which information people choose to consume and how it affects their beliefs. In the first experiment, respondents choose between optimistic and pessimistic article headlines related to the COVID-19 pandemic and ar ...

2024

Exploiting the Signal-Leak Bias in Diffusion Models

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Athanasios Fitsios, Martin Nicolas Everaert

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

2024

Timing and spatial selection bias in rapid extreme event attribution

Anthony Christopher Davison, Ophélia Mireille Anna Miralles

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

ELSEVIER2023

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

Aswin Suresh

We study socio-political systems in representative democracies. Motivated by problems that affect the proper functioning of the system, we build computational methods to answer research questions regarding the phenomena occurring in them. For each phenomen ...

EPFL2023

Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality

David Pascucci

Visual estimates of stimulus features are systematically biased toward the features of previously encountered stimuli. Such serial dependencies have often been linked to how the brain maintains perceptual continuity. However, serial dependence has mostly b ...

2023

The Impact of Data Persistence Bias on Social Media Studies

Tugrulcan Elmas

Social media studies often collect data retrospectively to analyze public opinion. Social media data may decay over time and such decay may prevent the collection of the complete dataset. As a result, the collected dataset may differ from the complete data ...

Assoc Computing Machinery2023

Educating Engineering Students to Address Bias and Discrimination Within Their Project Teams

Siara Ruth Isaac, Roland John Tormey, Nihat Kotluk

What training should engineering students receive to enable them to contribute to reducing bias, discrimination and the persistent lack of diversity in engineering? Collaboration is central to professional engineering work and, consequently, teamwork and g ...

2023

Information Processing Biases: The Effects of Negative Emotional Symptoms on Sampling Pleasant and Unpleasant Information

Steffen Alexander Herff

Although theories of emotion associate negative emotional symptoms with cognitive biases in information processing, they rarely specify the details. Here, we characterize cognitive biases in information processing of pleasant and unpleasant information, an ...

AMER PSYCHOLOGICAL ASSOC2022

Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

Tanja Christina Käser Jacober, Vinitra Swamy, Thiemo Wambsganss

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...

2022

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

Tao Lin

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or ...

ASSOC COMPUTING MACHINERY2022

Emotional empathy and engineering students’moral reasoning

Roland John Tormey, Nihat Kotluk

Although engineering education is often characterized as a rational activity, research suggests that emotions are vital for learning at all levels of education. In ethics education, there is evidence that including mild emotional information in case studie ...

2022

Advances in Bias-aware Recommendation on the Web

Mirko Marras

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describi ...

ASSOC COMPUTING MACHINERY2021

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

Mats Julius Stensrud

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that h ...

2021

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

Michele Ceriotti, Gareth Aneurin Tribello, Federico Giberti

Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, i ...

2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Scott William Pesme, Nicolas Henri Bernard Flammarion, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Controlling the rotation of drift tearing modes by biased electrode in ADITYA-U tokamak

Sameer Kumar, Abhijit Sen, Harshita Raj, Ankit Kumar, Rohit Kumar

The influence of background plasma poloidal rotation on the rotation frequency of the m/n = 2/1 drift tearing mode (DTM) has been studied in ADITYA-U tokamak. The poloidal rotation velocity of the background plasma in the ion diamagnetic direction is incre ...

2021

A systematic review of machine learning classification methodologies for modelling passenger mode choice

Michel Bierlaire, Ying Jin, Timothy Michael Hillel

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research ques ...

ELSEVIER SCI LTD2021

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

Katherine Colby Leonard, Robin Lewis Modini, Jenny Thomas, Fernando Perez Cruz, Julia Schmale, Christel Sybille Hassler, Irina Gorodetskaya, Sebastian Johannes Heinz-Josef Landwehr, Andrea Baccarini, Alireza Moallemi

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remai ...

2021

Tackling Peer-to-Peer Discrimination in the Sharing Economy

Boi Faltings, Maxime Rutagarama, Naman Goel

Sharing economy platforms such as Airbnb and Uber face a major challenge in the form of peer-to-peer discrimination based on sensitive personal attributes such as race and gender. As shown by a recent study under controlled settings, reputation systems can ...

ACM2020