Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank ...

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 ...

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 ...

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 ...

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 ...

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 ...

Urban data strongly favored by urban digitization and web 2.0 constitute the raw material whose sources often diverge. However, as far as we know, little or no work has been done to explore the sources and targeted applications for urban planning indicator ...

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 ...

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 ...

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 ...

Context. Stage IV weak lensing experiments will offer more than an order of magnitude leap in precision. We must therefore ensure that our analyses remain accurate in this new era. Accordingly, previously ignored systematic effects must be addressed.Aims. ...

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 ...

Making decisions is part and parcel of being human. Among a set of actions, we want to choose the one that has the highest reward. But the uncertainty of the outcome prevents us from always making the right decision. Making decisions under uncertainty can ...

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

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

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

Exploiting the Signal-Leak Bias in Diffusion Models

Timing and spatial selection bias in rapid extreme event attribution

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

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

The Impact of Data Persistence Bias on Social Media Studies

Information Retrieval Under Network Uncertainty: Robust Internet Ranking

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

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

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

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

Emotional empathy and engineering students’moral reasoning

Urban Data: Sources and Targeted Applications for Urban Planning Indicators Modelling

Advances in Bias-aware Recommendation on the Web

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

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

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

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

Multi-armed Bandits in Action