AI‑powered recommender systems and the preservation of personal autonomy

Recommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did not come with a thorough consideration of their ethical implications and, despite being a well-established technical domain, the potential impacts of RecSys on their users are still under-assessed. This paper aims at filling this gap in regards to one of the main impacts of RecSys: personal autonomy. We first describe how technology can affect human values and a suitable methodology to identify these effects and mitigate potential harms: Value Sensitive Design (VSD). We use VSD to carry out a conceptual investigation of personal autonomy in the context of a generic RecSys and draw on a nuanced account of procedural autonomy to focus on two components: competence and authenticity. We provide the results of our inquiry as a value hierarchy and apply it to the design of a speculative RecSys as an exampleUniversidad de Granada/ CBUAAgencia Estatal de Investigación (PID2019-104943RB-I00) FEDER/ Junta de Andalucía (B-HUM-64- UGR20

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

prototypical implementations ; working packages in project phase II

In this technical report, we present the concepts and first prototypical imple- mentations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge. The concepts and prototypes are based on the state of art analysis and identified requirements in the CSW report IV

state of the art analysis ; working packages in project phase II

In this report, we introduce our goals and present our requirement analysis for the second phase of the Corporate Semantic Web project. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Supporting users in understanding intelligent everyday systems

Intelligent systems have permeated many areas of daily life like communication, search, decision-making, and navigation, and thus present an important meeting point of people and artificial intelligence in practice. These intelligent everyday systems are in focus of this thesis. Intelligent everyday systems exhibit the characteristics of so-called complex systems as defined in cognitive science: They serve ill-defined user goals, change dynamically over time, and comprise a large number of interrelated variables whose dependencies are not transparent to users. Due to this complexity, intelligent everyday systems can violate established usability guidelines of user interface design like transparency, controllability and easy error correction. This may introduce uncertainty to interaction that users have to overcome in order to reach a goal. I introduce a perspective from cognitive science, where users do so through knowledge. The work presented in this thesis aims at assisting users in gaining this knowledge, or supporting users in understanding intelligent everyday systems, for example, through explanation, control, correction or feedback. To this end, the work included in this thesis makes three main contributions: First, I present a method for eliciting user need for support and informing adequate solutions through practical user problems with intelligent everyday systems in daily interaction. In a first phase, the presented method uses passive data collection to extract user problems with intelligent everyday systems through a combination of automated and manual analyses. In the second phase, these problems are then enriched and validated through active data collection to derive solutions for support. In addition, I report on the application of this method to uncover user problems with four popular commercial intelligent everyday systems (Facebook, Netflix, Google Maps and Google Assistant). Second, I introduce a conceptual framework for categorising and differentiating prevailing notionsin the field of how users should be supported in understanding intelligent systems related to what users seek to know, how they acquire knowledge, and what kind of knowledge they acquire. The presented framework can be used to make these notions explicit and thus introduces an overarching structure that abstracts from the field’s fractured terminological landscape. It aims at helping other researchers become aware of existing approaches and locate and reflect on their own work. Third, I present a number of case studies and arguments as an exploration of how users can be supported in the face of real-world challenges and trade-offs. My research reflects two possible perspectives to approach this question, a normative and a pragmatic one. As part of a critical reflection on the normative perspective, the work shows that explanations without information can similarly foster user trust in a system compared to real explanations, and discusses how user support can be exploited to deceive users. From the pragmatic perspective emerges a stage-based participatory design process that incorporates different stakeholder needs and a study assessing how support can be interwoven with users’ primary tasks. In summary, this thesis adopts a perspective on interaction with intelligent everyday systems, where understanding is a fundamental process towards reaching a user-set goal. On this basis, I introduce a research agenda for future work that incorporates the presented contributions and also includes challenges beyond the scope of this work, such as considering user empowerment. I hope that this agenda, along with the presented method, framework and design exploration, will help future work to shape interaction with intelligent everyday systems in a way that allows people to use them better, and to better ends and outcomes.Intelligente Systeme haben Einzug in viele Bereiche des täglichen Lebens wie Kommunikation, Informationssuche, Entscheidungsfindung, und Navigation erhalten und stellen damit einen wichtigen Berührungspunkt von Menschen und künstlicher Intelligenz in der Praxis dar. Solche intelligenten Alltagssysteme stehen im Fokus dieser Arbeit. Intelligente Alltagssysteme weisen die Charakteristika von sogenannten komplexen Systemen aus der Kognitionsforschung auf: Sie dienen unscharfen Nutzerzielen, verändern sich dynamisch über die Zeit, und beinhalten eine große Anzahl an miteinander verknüpften Variablen, deren Wechselbeziehungen für Nutzer nicht erkennbar sind. Auf Grund dieser Komplexität können intelligente Alltagssysteme bewährte Richtlinien zur Gestaltung von nutzerfreundlichen Benutzeroberflächen verletzen, beispielsweise Transparenz, Kontrollierbarkeit, und einfache Fehlerbehebung. Dies kann bei der Interaktion zu Unsicherheit führen, die Nutzer auf dem Weg zu einem Ziel überwinden müssen. Ich führe eine Perspektive aus der Kognitionsforschung ein, nach welcher Nutzer dies durch Wissen tun. Die hier präsentierten Arbeiten haben zum Ziel, Nutzern beim Erlangen dieses Wissens zu helfen, oder Nutzerverständnis von intelligenten Alltagssystemen zu unterstützen, beispielsweise durch Erklärung, Kontrolle, Korrektur oder Rückmeldung an das System. Hierzu leisten die vorgestellten Arbeiten hauptsächlich drei Beiträge: Ich präsentiere zunächst eine Methode, um das Nutzerbedürnis nach Unterstützung zu ermitteln und entsprechende Lösungen zu informieren. Die Methode identifiziert dazu praktische Nutzerprobleme mit intelligenten Alltagssystemen im täglichen Gebrauch. In einer ersten Phase werden diese Probleme auf Grund von passiver Datenerhebung unter Verwendung automatisierter und manueller Analysemethoden extrahiert. In der zweiten Phase werden die ermittelten Problemedurch aktive Datenerhebung angereichert und validiert, um Lösungen zur Unterstützung abzuleiten. Daneben berichte ich von der Anwendung dieser Methode, um Nutzerprobleme in vier verbreiteten kommerziellen intelligenten Alltagssystemen (Facebook, Netflix, Google Maps und Google Assistant) aufzudecken. Danach führe ich ein konzeptuelles Framework ein, mit dem im Feld vorherrschende Annahmen, wie Nutzerverständnis von intelligenten Alltagssystemen unterstützt werden sollte, klassifiziert und differenziert werden können. Diese Annahmen beziehen sich darauf, welches Wissen Nutzer erlangen wollen, wie sie dieses Wissen erlangen, und um welche Art von Wissen es sich handelt. Durch das Framework können die jeweiligen Annahmen explizit gemacht werden. Es schafft so eine übergreifende Struktur, die von der Fülle und Diversität der im Feld verwendeten Begrifflichkeiten abstrahiert. Das Framework kann anderen Forschern dabei helfen, sich über bestehende Ansätze bewusst zu werden, und ihre eigene Arbeit zu verorten und zu reflektieren. Zum Dritten bringe ich eine Reihe von Fallbeispielen und Argumenten an, die explorieren, wie Nutzer angesichts von Einschränkungen und Abwägungen in der Praxis unterstützt werden können. Meine Forschung spiegelt dabei zwei mögliche Sichtweisen auf diese Frage wider, eine normative und eine pragmatische. Im Zuge einer kritischen Betrachtung der normativen Sichtweise zeigt diese Arbeit, dass Erklärungen ohne Informationsgehalt in ähnlicher Weise Vertrauen in ein System hervorrufen können wie richtige Erklärungen. In diesem Zusammenhang wird weiterhin diskutiert, wie Unterstützung gezielt zur Täuschung von Nutzern missbraucht werden kann. Aus der pragmatischen Sichtweise geht in dieser Arbeit ein stufenförmiger partizipatorischer Designprozess hervor, der die verschiedenen Interessen in der Praxis Beteiligter berücksichtigt. Zudem wird in einer Studie untersucht, wie Unterstützung von Verständnis mit der Primäraufgabe von Nutzern verknüpft werden kann. Zusammenfassend nimmt diese Arbeit eine Perspektive auf Interaktion mit intelligenten Alltagssystemen ein, die Verstehen als grundlegenden Prozess auf dem Weg zu einem Nutzerziel begreift. Basierend darauf stelle ich eine Forschungsagenda vor, die die präsentierten Publikationen einschließt und zudem Herausforderungen über den Rahmen dieser Arbeit hinaus beinhaltet, wie beispielsweise die Einbeziehung von“Nutzer-Empowerment”. Ich hoffe, dass diese Agenda, die vorgestellte Methode, das Framework und die Erkenntnisse aus der Exploration möglicher Designansätze zukünftiger Forschung hilft, Interaktion mit intelligenten Systemen im Alltag zu gestalten – so, dass Nutzer sie besser und zu besseren Zwecken verwenden können

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad