The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer

Learning and reasoning over graphs is increasingly done by means of probabilistic models, e.g. exponential random graph models, graph embedding models, and graph neural networks. When graphs are modeling relations between people, however, they will inevitably reflect biases, prejudices, and other forms of inequity and inequality. An important challenge is thus to design accurate graph modeling approaches while guaranteeing fairness according to the specific notion of fairness that the problem requires. Yet, past work on the topic remains scarce, is limited to debiasing specific graph modeling methods, and often aims to ensure fairness in an indirect manner. We propose a generic approach applicable to most probabilistic graph modeling approaches. Specifically, we first define the class of fair graph models corresponding to a chosen set of fairness criteria. Given this, we propose a fairness regularizer defined as the KL-divergence between the graph model and its I-projection onto the set of fair models. We demonstrate that using this fairness regularizer in combination with existing graph modeling approaches efficiently trades-off fairness with accuracy, whereas the state-of-the-art models can only make this trade-off for the fairness criterion that they were specifically designed for

The Elemental Analysis of Glass Beads

Ancient glass beads as a window to the ancient world Glass beads, both beautiful and portable, have been produced and traded globally for thousands of years. Modern archaeologists study these artifacts through sophisticated methods that analyze the glass composition, a process which can be utilized to trace bead usage through time and across regions. This book publishes open-access compositional data obtained from laser ablation – inductively coupled plasma – mass spectrometry, from a single analytical laboratory, providing a uniquely comparative data set. The geographic range includes studies of beads produced in Europe and traded widely across North America and beads from South and Southeast Asia traded around the Indian Ocean and beyond. The contributors provide new insight on the timing of interregional interactions, technologies of bead production and patterns of trade and exchange, using glass beads as a window to the past. This volume will be a key reference for glass researchers, archaeologists, and any scholars interested in material culture and exchange; it provides a wide range of case studies in the investigation and interpretation of glass bead composition, production and exchange since ancient times. Contributors: Bernard Gratuze (Institut de Recherche sur les ArchéoMATériaux, Centre Ernest-Babelon, UMR 5060 CNRS/Université d'Orléans), Alicia L. Hawkins (University of Toronto Mississauga), Elliot H. Blair (University of Alabama), Jessica Dalton-Carriger (Roane State Community College), Lee M. Panich (Santa Clara University), Thomas R. Fenn (The University of Oklahoma), Alison K. Carter (University of Oregon), Jennifer Craig (McGill University), Mark Aldenderfer (University of California, Merced), Mudit Trivedi (Stanford University), Lindsey Trombetta (The University of Texas at Austin), Jonathan R. Walz (The Field Museum / SIT-Graduate Institute), Akshay Sarathi (Florida Atlantic University), Carla Klehm (University of Arkansas), Marilee Wood (University of the Witwatersrand), Katherine A. Larson (Corning Museum of Glass), Heather Walder (The Field Museum / University of Wisconsin – La Crosse), Laure Dussubieux (The Field Museum) Supplementary Material 'The Elemental Analysis of Glass Beads' Ebook available in Open Access. This publication is GPRC-labeled (Guaranteed Peer-Reviewed Content)

The Elemental Analysis of Glass Beads

Ancient glass beads as a window to the ancient world Glass beads, both beautiful and portable, have been produced and traded globally for thousands of years. Modern archaeologists study these artifacts through sophisticated methods that analyze the glass composition, a process which can be utilized to trace bead usage through time and across regions. This book publishes open-access compositional data obtained from laser ablation – inductively coupled plasma – mass spectrometry, from a single analytical laboratory, providing a uniquely comparative data set. The geographic range includes studies of beads produced in Europe and traded widely across North America and beads from South and Southeast Asia traded around the Indian Ocean and beyond. The contributors provide new insight on the timing of interregional interactions, technologies of bead production and patterns of trade and exchange, using glass beads as a window to the past. This volume will be a key reference for glass researchers, archaeologists, and any scholars interested in material culture and exchange; it provides a wide range of case studies in the investigation and interpretation of glass bead composition, production and exchange since ancient times. Contributors: Bernard Gratuze (Institut de Recherche sur les ArchéoMATériaux, Centre Ernest-Babelon, UMR 5060 CNRS/Université d'Orléans), Alicia L. Hawkins (University of Toronto Mississauga), Elliot H. Blair (University of Alabama), Jessica Dalton-Carriger (Roane State Community College), Lee M. Panich (Santa Clara University), Thomas R. Fenn (The University of Oklahoma), Alison K. Carter (University of Oregon), Jennifer Craig (McGill University), Mark Aldenderfer (University of California, Merced), Mudit Trivedi (Stanford University), Lindsey Trombetta (The University of Texas at Austin), Jonathan R. Walz (The Field Museum / SIT-Graduate Institute), Akshay Sarathi (Florida Atlantic University), Carla Klehm (University of Arkansas), Marilee Wood (University of the Witwatersrand), Katherine A. Larson (Corning Museum of Glass), Heather Walder (The Field Museum / University of Wisconsin – La Crosse), Laure Dussubieux (The Field Museum) Supplementary Material 'The Elemental Analysis of Glass Beads' Ebook available in Open Access. This publication is GPRC-labeled (Guaranteed Peer-Reviewed Content)

Microdynamics in diverse teams:A review and integration of the diversity and stereotyping literatures

Research on the consequences of diversity in teams continues to produce inconsistent results. We review the recent developments in diversity research and identify two shortcomings. First, an understanding of the microdynamics affecting processes and outcomes in diverse teams is lacking. Second, diversity research has tended to treat different social categories as equivalent and thus not considered how members’ experiences may be affected by their social category membership. We address these shortcomings by reviewing research on stereotypes, which indicates that stereotypes initiate reinforcing microdynamics among (a) attributions of a target team member’s warmth and competence, (b) perceiving members’ behavior toward the target team member, and (c) the target team member’s behavior. Our review suggests that perceivers’ impression formation motivation is the key determinant of the extent to which perceivers continue to treat a target based on categorization. On the basis of our review, we provide an integrative perspective and corresponding model that outlines these MIcrodynamics of Diversity and Stereotyping in Teams (MIDST) and indicates how stereotyping can benefit as well as harm team functioning. We discuss how this integrative perspective on the MIDST relates to the social categorization and the information/decision-making perspective, set a research agenda, and discuss the managerial implications

Fairness-aware Optimal Graph Filter Design

Graphs are mathematical tools that can be used to represent complex real-world interconnected systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonstrated that ML over graphs amplifies the already existing bias towards certain under-represented groups in various decision-making problems due to the information aggregation over biased graph structures. Faced with this challenge, here we take a fresh look at the problem of bias mitigation in graph-based learning by borrowing insights from graph signal processing. Our idea is to introduce predesigned graph filters within an ML pipeline to reduce a novel unsupervised bias measure, namely the correlation between sensitive attributes and the underlying graph connectivity. We show that the optimal design of said filters can be cast as a convex problem in the graph spectral domain. We also formulate a linear programming (LP) problem informed by a theoretical bias analysis, which attains a closed-form solution and leads to a more efficient fairness-aware graph filter. Finally, for a design whose degrees of freedom are independent of the input graph size, we minimize the bias metric over the family of polynomial graph convolutional filters. Our optimal filter designs offer complementary strengths to explore favorable fairness-utility-complexity tradeoffs. For performance evaluation, we conduct extensive and reproducible node classification experiments over real-world networks. Our results show that the proposed framework leads to better fairness measures together with similar utility compared to state-of-the-art fairness-aware baselines.Comment: 12 pages, 3 figures, 9 tables. arXiv admin note: text overlap with arXiv:2303.1145