Evaluating Recommender Systems Qualitatively: A survey and Comparative Analysis

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsRecommender systems have improved users' online quality of life by helping them find interesting and valuable items within a large item set. Most recommender system validation research has focused on accuracy metrics, studying the differences between the predicted and actual user ratings. However, recent research has found accuracy to underperform when systems go live, mainly due to accuracy’s inability to validate recommendation lists as a single entity, and shifted to evaluating recommender systems using "beyond-accuracy" metrics, like novelty and diversity. In this dissertation, we summarize and organize the leading research regarding the definitions and objectives of the beyond-accuracy metrics. Such metrics include coverage, diversity, novelty, serendipity, unexpectedness, utility, and fairness. The behaviors and relationships of these metrics are analyzed using four different models, two concerning the items characteristics (item-based) and two regarding the user behaviors (user-based). Furthermore, a new metric is proposed that allows the comparison of different models considering their overall beyond-accuracy performance. Using this metric, a reraking approach is designed to improve the performance of a system, aiming to achieve better recommendations. The impact of the reranking technique on each metric and algorithm is studied, and the accuracy and non-accuracy performance of each system is compared. We realized that, although the reranking technique can increase most beyond-accuracy metrics, the accuracy of that system starts to worsen due to the negative correlation between these two dimensions. We also found that item-based models tend to achieve much lower values of coverage and diversity than userbased models

Runtime Monitoring of Human-centric Requirements in Machine Learning Components: A Model-driven Engineering Approach

As machine learning (ML) components become increasingly integrated into software systems, the emphasis on the ethical or responsible aspects of their use has grown significantly. This includes building ML-based systems that adhere to human-centric requirements, such as fairness, privacy, explainability, well-being, transparency and human values. Meeting these human-centric requirements is not only essential for maintaining public trust but also a key factor determining the success of ML-based systems. However, as these requirements are dynamic in nature and continually evolve, pre-deployment monitoring of these models often proves insufficient to establish and sustain trust in ML components. Runtime monitoring approaches for ML are potentially valuable solutions to this problem. Existing state-of-the-art techniques often fall short as they seldom consider more than one human-centric requirement, typically focusing on fairness, safety, and trust. The technical expertise and effort required to set up a monitoring system are also challenging. In my PhD research, I propose a novel approach for the runtime monitoring of multiple human-centric requirements. This approach leverages model-driven engineering to more comprehensively monitor ML components. This doctoral symposium paper outlines the motivation for my PhD work, a potential solution, progress so far and future plans

Detecting discriminatory risk through data annotation based on Bayesian inferences

Thanks to the increasing growth of computational power and data availability, the research in machine learning has advanced with tremendous rapidity. Nowadays, the majority of automatic decision making systems are based on data. However, it is well known that machine learning systems can present problematic results if they are built on partial or incomplete data. In fact, in recent years several studies have found a convergence of issues related to the ethics and transparency of these systems in the process of data collection and how they are recorded. Although the process of rigorous data collection and analysis is fundamental in the model design, this step is still largely overlooked by the machine learning community. For this reason, we propose a method of data annotation based on Bayesian statistical inference that aims to warn about the risk of discriminatory results of a given data set. In particular, our method aims to deepen knowledge and promote awareness about the sampling practices employed to create the training set, highlighting that the probability of success or failure conditioned to a minority membership is given by the structure of the data available. We empirically test our system on three datasets commonly accessed by the machine learning community and we investigate the risk of racial discrimination.Comment: 11 pages, 8 figure

Fairness in rankings and recommenders : Models, methods and research directions

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommendation systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. This tutorial aims at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are three-fold: (a) to provide a solid framework on a novel, quickly evolving, and impactful domain, (b) to present related methods and put them into perspective, and (c) to highlight challenges and research paths for researchers and practitioners that work in data management and applications.Peer reviewe

Perfectly Parallel Fairness Certification of Neural Networks

International audienceRecently, there is growing concern that machine-learned software, which currently assists or even automates decision making, reproduces, and in the worst case reinforces, bias present in the training data. The development of tools and techniques for certifying fairness of this software or describing its biases is, therefore, critical. In this paper, we propose a perfectly parallel static analysis for certifying fairness of feed-forward neural networks used for classification of tabular data. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased input space regions. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool called libra and demonstrate its effectiveness on neural networks trained on popular datasets

Algorithms for Social Good: A Study of Fairness and Bias in Automated Data-Driven Decision-Making Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Digitalization of the individual : consequences, design, and behavior

In the past decades, digitalization has increasingly influenced our daily lives and habits in almost all areas and has even become indispensable for individuals, organizations, and society. The interactions between individuals and organizations have changed significantly as digitalization extends the boundaries of organizations to the point at which it affects individuals. Consequently, new research efforts and better understanding are essential to understand how the behavior of individuals is affected by the use of digital technologies, how customers demands change, and how the purchasing process of organizations needs to be adapted. Currently, the literature on digital transformation is mainly treating the organizational perspective. Nevertheless, organizations should not neglect the individual perspective as it is essential to understand customer needs and their consequences affected by digital technologies. Matt et al. (2019)1 present a holistic research framework with three research perspectives for the digitalization of the individual. This framework encompasses the behavior of individuals, the design of information systems, and the consequences that digitalization entails. Additionally, this research framework suggests that a digitized individual can take on different roles. The dissertation uses this framework of Matt et al. (2019)1 to structure and classify the covered contents and research objectives. The aim of this dissertation is to contribute to a comprehensive overview for organizations to understand their customers concerns regarding digital technologies, which design options they have to address these concerns, and how it influences their behavior to realize the potential of the technologies or reduce their harms. Therefore, this work applies pluralistic methodological approaches (qualitative methods, e.g., semi-structured interviews and qualitative content analysis, and quantitative methods, e.g., quantitative decision models and data collection from online questionnaires). With that, the dissertation provides novel insights for organizations to better implement digital technologies by regarding the consequences for individuals and the behavior of individuals. First, to contribute to an understanding of the negative consequences digitalization can bring along for individuals, part A of this dissertation presents two research articles that focus on the concerns of individuals. The research papers P1 and P2 show in two different domains what individuals are concerned about when using digital technologies and what prevents individuals from using them. Therefore, this dissertation presents knowledge about the fears and concerns of the individuals have and offers starting points to develop responsible and transparent digital technologies that address the concerns of the individuals. Second, to contribute to design approaches for information systems that enable organizations to increase customer satisfaction with digital products and services, part B presents design approaches that organizations can use to address individuals perceived consequences and change their behavior using digital technologies. Both research papers in part B present quantitative decision models as decision support for organizations. This dissertation offers two design approaches that provide organizations with information on designing technologies to serve digitized individuals and foster them better to make well-founded decisions when introducing digital technologies. Third, to contribute to the understanding of why and how individuals behave in certain ways and how this behavior can be influenced, Part C examines the behavior of individuals when using digital technologies. Research paper P5 develops a metric to better explore the privacy paradox. With that, this dissertation offers a basis, especially to researchers and individuals, to prevent unwanted behavior when using digital technologies. To sum up, this dissertation contributes to scientific knowledge in research on the digitalization of the individual and thus addresses a subject of fundamental importance in this digital age. The models and approaches developed in this dissertation explore ways to improve conditions for the digitized individual at all three research perspectives with equal regard for the individual as itself and the individual as a customer.In den vergangenen Jahrzehnten hat die Digitalisierung zunehmend unseren Alltag und unsere Gewohnheiten in nahezu Bereichen des Lebens beeinflusst und ist damit für Individuen, Organisationen und die Gesellschaft unverzichtbar geworden. So hat sich die Beziehung zwischen Individuen und Organisationen erheblich verändert, da die Digitalisierung die Organisationsgrenzen aufweicht und ihre Kund:innen mehr integriert. Folglich sind neue Forschungsanstrengungen und ein besseres Verständnis erforderlich, um nachvollziehen zu können, wie das Verhalten von Individuen durch den Einsatz digitaler Technologien beeinflusst wird, wie sich die Anforderungen von Kund:innen ändern und wie der Kaufprozess von Organisationen angepasst werden muss. Derzeit wird in der Literatur zum Themengebiet der digitalen Transformation hauptsächlich die organisationale Perspektive behandelt. Nichtsdestotrotz sollten Organisationen die individuelle Perspektive nicht vernachlässigen. Sie ist grundlegend, um die Kund:innenbedürfnisse, die durch digitale Technologien beeinflusst werden, und deren Folgen zu verstehen. Matt et al. (2019) stellen einen ganzheitlichen Forschungsrahmen mit drei Forschungsperspektiven für die Digitalisierung des Individuums vor. Dieser umfasst das Verhalten der Individuen, die Gestaltung von Informationssystemen und die Konsequenzen, die die Digitalisierung für Individuen mit sich bringen kann. Zusätzlich zeigt dieser, dass ein digitalisiertes Individuum verschiedene Rollen einnehmen kann. Die Dissertation nutzt das Framework von Matt et al. (2019), um die Inhalte und Forschungsziele zu strukturieren und einzuordnen. Ziel dieser Dissertation ist es, einen Beitrag zu einem umfassenden Überblick für Organisationen zu leisten, um die Individuen im Zuge der Digitalisierung zu verstehen. Dabei wird untersucht, welche Bedenken ihre Kund:innen in Bezug auf digitale Technologien haben, welche Gestaltungsmöglichkeiten sie haben, um diese Bedenken zu adressieren, und wie es das Verhalten von Kund:innen beeinflusst. Dadurch können sie das Potenzial dieser Technologien realisieren oder ihre Schäden reduzieren. Diese Arbeit wendet eine Vielzahl an methodischen Ansätzen an (qualitative Methoden, z.B. halbstrukturierte Interviews und qualitative Inhaltsanalyse, und quantitative Methoden, z.B. quantitative Entscheidungsmodelle und Datenerhebung aus Online-Fragebögen). Damit liefert die Dissertation neue Erkenntnisse für Organisationen, um digitale Technologien besser zu implementieren, indem sie die Konsequenzen für Individuen und das Verhalten von Individuen betrachtet. Um erstens einen Beitrag zum besseren Verständnis der negativen Folgen, die die Digitalisierung für den Einzelnen mit sich bringen kann, zu leisten, umfasst Teil A dieser Dissertation zwei Forschungsartikel, die sich mit den Bedenken des Einzelnen beschäftigen. Die Forschungsartikel P1 und P2 zeigen in zwei unterschiedlichen Bereichen, welche Bedenken Individuen bei der Nutzung digitaler Technologien haben und was Individuen davon abhält, diese zu nutzen. Daher präsentiert diese Dissertation Wissen über die Ängste und Bedenken der Individuen und bietet Ansatzpunkte, um verantwortungsvolle und transparente digitale Technologien zu entwickeln. Um zweitens einen Beitrag zu Gestaltungsansätzen für Informationssysteme zu leisten, werden in Teil B Gestaltungsansätze vorgestellt, mit denen Organisationen die wahrgenommenen Konsequenzen für Individuen adressieren und das Verhalten im Umgang mit digitalen Technologien ändern können. Diese ermöglichen es Organisationen die Kund:innenzufriedenheit bei der Nutzung von digitalen Produkten und Dienstleistungen zu erhöhen. Beide Forschungsarbeiten in Teil B stellen quantitative Entscheidungsmodelle als Entscheidungshilfe für Organisationen vor. Diese Dissertation bietet zwei Gestaltungsansätze, die Organisationen Informationen zur Gestaltung von Informationssystemen liefern und sie dabei unterstützen, fundierte Entscheidungen bei der Einführung digitaler Technologien zu treffen. Drittens, um zum Verständnis beizutragen, warum und wie sich Individuen auf bestimmte Weise verhalten und wie dieses Verhalten beeinflusst werden kann, wird in Teil C das Verhalten von Individuen bei der Nutzung digitaler Technologien untersucht. P5 entwickelt eine Metrik, um das Privacy-Paradoxon besser zu erforschen. Damit bietet diese Dissertation eine Grundlage, insbesondere für Forscherinnen und Forscher sowie Individuen, um unerwünschtes Verhalten bei der Nutzung digitaler Technologien zu verhindern. Zusammenfassend lässt sich sagen, dass diese Dissertation wissenschaftliche Erkenntnisse zur Erforschung der Digitalisierung des Individuums leistet und damit ein Thema von grundlegender Bedeutung im digitalen Zeitalter behandelt. Die in dieser Dissertation entwickelten Modelle und Ansätze zeigen Wege auf, wie die Bedingungen für das digitalisierte Individuum auf allen drei Forschungsperspektiven verbessert werden können