Trading-off price for data quality to achieve fair online allocation

We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the protected attributes -- which is often unrealistic in practice. Instead they can purchase data that help estimate them from sources of different quality; and hence reduce the fairness penalty at some cost. We model this problem as a multi-armed bandit problem where each arm corresponds to the choice of a data source, coupled with the online allocation problem. We propose an algorithm that jointly solves both problems and show that it has a regret bounded by $\mathcal{O}(\sqrt{T})$. A key difficulty is that the rewards received by selecting a source are correlated by the fairness penalty, which leads to a need for randomization (despite a stochastic setting). Our algorithm takes into account contextual information available before the source selection, and can adapt to many different fairness notions. We also show that in some instances, the estimates used can be learned on the fly

Market Engineering

This open access book provides a broad range of insights on market engineering and information management. It covers topics like auctions, stock markets, electricity markets, the sharing economy, information and emotions in markets, smart decision-making in cities and other systems, and methodological approaches to conceptual modeling and taxonomy development. Overall, this book is a source of inspiration for everybody working on the vision of advancing the science of engineering markets and managing information for contributing to a bright, sustainable, digital world. Markets are powerful and extremely efficient mechanisms for coordinating individuals’ and organizations’ behavior in a complex, networked economy. Thus, designing, monitoring, and regulating markets is an essential task of today’s society. This task does not only derive from a purely economic point of view. Leveraging market forces can also help to tackle pressing social and environmental challenges. Moreover, markets process, generate, and reveal information. This information is a production factor and a valuable economic asset. In an increasingly digital world, it is more essential than ever to understand the life cycle of information from its creation and distribution to its use. Both markets and the flow of information should not arbitrarily emerge and develop based on individual, profit-driven actors. Instead, they should be engineered to serve best the whole society’s goals. This motivation drives the research fields of market engineering and information management. With this book, the editors and authors honor Professor Dr. Christof Weinhardt for his enormous and ongoing contribution to market engineering and information management research and practice. It was presented to him on the occasion of his sixtieth birthday in April 2021. Thank you very much, Christof, for so many years of cooperation, support, inspiration, and friendship

Metodologie e strumenti per lo studio del bias di genere nei documenti di testo

Gli studi riguardanti problemi di comprensione del linguaggio naturale da parte di modelli ricompre un importante ruolo nell’ambito delle tecnologie digitali. In questo lavoro verranno affrontati dei possibili metodi e strumenti per analizzare tali problemi, con una maggiore attenzione al tema del bias di genere. Si prenderanno in considerazione dati digitali sotto forma testuale e si cercherà di ottenere dei modelli che abbiamo la capacità di comprendere il linguaggio naturale e la sua semantica per poi applicarli ad un eventuale studio riguardante in bias di genere all’interno del testo. Il principale strumento del natural language processing è il word embedding, ossia uno strumento che ha lo scopo di estrarre le informazioni semantiche e sintattiche da un testo, attraverso la trasformazione di quest’ultimo in uno spazio vettoriale, dove ogni parola è rappresentata da vettori di numeri naturali, i quali più si trovano tanto più vicini quanto ricorrono nello stesso spazio semantico. Questa idea si fonda sulla teoria della semantica distribuzionale, ossia che il lessico viene concepito come uno spazio vettoriale dove le parole sono separate da distanze che dipendono dal loro grado di similitudine. Si vedrà che esistono differenti modi per individuare il migliore modello, dipendentemente dall’obiettivo che ci si pone; si scoprirà che la dimensione dei dati utilizzati risulterà molto importante per costruire dei modelli in grado di cogliere la semantica del testo e si individueranno delle possibili tecniche per studiare il bias di genere all’interno dei dati

Fairness in Information Access Systems

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space

The Right to Housing in the City

A global urban housing crisis marked by gross unaffordability, economic, social, cultural, and political displacements, increasing homelessness, and socio-spatial segregation and exclusion has taken hold. Deprivations are occurring in states across the development spectrum, and urban housing tensions are becoming topics of daily conversation if not outright protest. Much of the difficulty can be attributed to the financialization of housing and the global competitive pressures with which cities increasingly contend. An ‘urban turn’ has even become evident in the global discourse and practice, and this is apparent in the adoption of an urban specific UN Sustainable Development Goal in 2015, Goal 11, which commits to make cities inclusive, safe, resilient, and sustainable, with ambitious targets to be achieved by 2030. Local governments have become more assertive with respect to implementing international norms and forming transnational networks of cooperation. In these respects, a few notable scholars have begun to examine the role of cities in international law, and this body of literature is now steadily growing. This PhD thesis aims to make a significant contribution by focusing specifically on the question of housing in cities and by emphasizing the social dimensions of sustainable development. Its central argument is for a human right to housing in the city with a duty to act collectively to ensure this right. Analysis of the scope and content of the right to housing finds its present interpretation narrow and wanting in the face of contemporary urban challenges. There is an urgent need to reorient the right to housing in order to ensure access to urban space and to address the dispossessions taking place in cities