Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system

Reducing Risk in Digital Self-Control Tools: Design Patterns and Prototype

Many users take advantage of digital self-control tools to self-regulate their device usage through interventions such as timers and lockout mechanisms. One of the major challenges faced by these tools is the user reacting against their self-imposed constraints and abandoning the tool. Although lower-risk interventions would reduce the likelihood of abandonment, previous research on digital self-control tools has left this area of study relatively unexplored. In response, this paper contributes two foundational principles relating risk and effectiveness; four widely applicable novel design patterns for reducing risk of abandonment of digital self-control tools (continuously variable interventions, anti-aging design, obligatory bundling of interventions, and intermediary control systems); and a prototype digital self-control tool that implements these four low-risk design patterns

Empowering Responsible Online Gambling by Real-time Persuasive Information Systems

Online gambling, unlike other mediums of problem- atic and addictive behaviours, such as tobacco and alcohol, offers unprecedented opportunities for building information systems that are able to monitor and understand a user’s behaviour in real-time and adapt persuasive messages and interactions that would fit their personal profile and usage context. Online gambling industry usually provides Application Programming Interfaces (APIs) meant mainly to enable third-party applications to network with their gambling services and enhance a user’s gambling experience. In this industrial practice and experience paper, we advocate that such API’s can also be used to retrieve gamblers’ online data, such as browsing and betting history, promotions and available offers and use it to build more intel- ligent and proactive responsible gambling information systems. We report on our industrial experience in this field and make the argument that data available for persuasive marketing and usability should, under specific usage conditions, also be made available for responsible gambling information systems. This principle would provide equal opportunities for both directions. We discuss the psychological foundations of our proposed solution and the risks and challenges typically found when building such a software-assisted intervention, persuasion and emotion regulation technology. We also shed light on its potential implications from the perspectives of social corporate responsibility and data protection. We finally propose a conceptual architecture to demonstrate our vision and explain how it can be implemented. In the wider context, the paper is meant to provide insights on building behavioural awareness and regulation information systems in relation to problematic digital media usage

The AI Ethics Principle of Autonomy in Health Recommender Systems

The application of health recommender systems (HRSs) in the mobile-health (m-health) industry, especially for healthy active aging, has grown exponentially over the past decade. However, no research has been conducted on the ethical implications of HRSs and the ethical principles for their design. This paper aims to fill this gap and claims that an ethically informed re-definition of the AI ethics principle of autonomy is needed to design HRSs that adequately operationalize (that is, respect and promote) individuals’ autonomy over ageing. To achieve this goal, after having clarified the state-of-the-art on HRSs, I present the reasons underlying the need to focus on autonomy as a prominent ethical issue and principle for the design of HRSs. Then, I pursue an inquiry on autonomy in HRSs and show that HRSs can both promote individuals’ autonomy and undermine it, also leading to phenomena of passive ageing. In particular, I claim that this is also due to the concept of autonomy underlying the debate on HRSs-based m-health, which is sometimes misleading, as it mainly coincides with informational self-determination. Using ethical reasoning, I shed light on a more complex account of autonomy and I redefine the AI ethics principle of autonomy accordingly. I show that autonomy and informational self-determination do not overlap. I also show that autonomy encompasses also a socio-relational dimension and that it requires both authenticity conditions and social recognition conditions. Finally, I analyze the implications of my ethical redefinition of autonomy for the design of autonomy-enabling HRSs for healthy active ageing

Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems

Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption. A natural following question is whether current recommendation algorithms are manipulating user preferences. If so, can we measure the manipulation level? In this paper, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios. The framework consists of four stages, initial preference calculation, training data collection, algorithm training and interaction, and metrics calculation that involves two proposed metrics. We benchmark some representative recommendation algorithms in both synthetic and real-world datasets under the proposed framework. We have observed that a high online click-through rate does not necessarily mean a better understanding of user initial preference, but ends in prompting users to choose more documents they initially did not favor. Moreover, we find that the training data have notable impacts on the manipulation degrees, and algorithms with more powerful modeling abilities are more sensitive to such impacts. The experiments also verified the usefulness of the proposed metrics for measuring the degree of manipulations. We advocate that future recommendation algorithm studies should be treated as an optimization problem with constrained user preference manipulations.Comment: 33 pages, 11 figures, 4 tables, ACM Transactions on Information System

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

With the increase in data volume, velocity and types, intelligent human-agent systems have become popular and adopted in different application domains, including critical and sensitive areas such as health and security. Humans’ trust, their consent and receptiveness to recommendations are the main requirement for the success of such services. Recently, the demand on explaining the recommendations to humans has increased both from humans interacting with these systems so that they make an informed decision and, also, owners and systems managers to increase transparency and consequently trust and users’ retention. Existing systematic reviews in the area of explainable recommendations focused on the goal of providing explanations, their presentation and informational content. In this paper, we review the literature with a focus on two user experience facets of explanations; delivery methods and modalities. We then focus on the risks of explanation both on user experience and their decision making. Our review revealed that explanations delivery to end-users is mostly designed to be along with the recommendation in a push and pull styles while archiving explanations for later accountability and traceability is still limited. We also found that the emphasis was mainly on the benefits of recommendations while risks and potential concerns, such as over-reliance on machines, is still a new area to explore

Ethical and legal implications of using AI-powered recommendation systems in streaming services

Recommendation engines are commonly used in the entertainment industry to keep users glued in front of their screens. These engines are becoming increasingly sophisticated as machine learning tools are being built into ever-more complex AI-driven systems that enable providers to effectively map user preferences. The utilization of AI-powered tools, however, has serious ethical and legal implications. Some of the emerging issues are already being addressed by ethical codes, developed by international organizations and supranational bodies. The present study aimed to address the key challenges posed by AI-powered content recommendation engines. Consequently, this paper introduces the relevant rules present in the existing ethical guidelines and elaborates on how they are to be applied within the streaming industry. The paper strives to adopt a critical standpoint towards the provisions of the ethical guidelines in place, arguing that adopting a one-size-fits all approach is not effective due to the specificities of the content distribution industry

Enabling Responsible Online Gambling by Real-time Persuasive Technologies

Online gambling, unlike other offline addiction forms, provides unprecedented opportunities for monitoring users’ behaviour in real-time, along with the ability to adapt persuasive interactions and messages that would match the gamblers usage and personal context. Online gambling industry usually offers Application Programming Interfaces (APIs) that are mainly intended to allow third-party applications to interact with their services and enhance user’s experience. In this paper, we claim that such API’s can also be utilised to retrieve gamblers’ online data, such as browsing and betting history and other available offers, and use it to build more proactive and intelligent responsible gambling systems. We report on our experience in this field and make the argument that the available data for persuasive marketing and usability should, under certain usage conditions, also be made available for responsible online gambling services. We discuss the psychological foundations of our proposed approach and the risks and challenges typically resulted when building such a software-assisted intervention, persuasion and emotion regulation technology. We also explain the potential impact of corporate social responsibility and data protection prospects. Furthermore, we explore the required principles that should be followed by the gambling industry for enabling responsible online gambling. We finally propose a conceptual architecture to show our vision and explain how it can be implemented. In the broader context, the paper is intended to provide insights on building behavioural awareness and regulation information systems related to problematic digital media usage. Keywords: Persuasive technologies, responsible online gambling, gambling data availability, corporate social responsibility

Psychological research in the digital age

The smartphone has become an important personal companion in our daily lives. Each time we use the device, we generate data that provides information about ourselves. This data, in turn, is valuable to science because it objectively reflects our everyday behavior and experiences. In this way, smartphones enable research that is closer to everyday life than traditional laboratory experiments and questionnaire-based methods. While data collected with smartphones are increasingly being used in the field of personality psychology, new digital technologies can also be leveraged to collect and analyze large-scale unobtrusively sensed data in other areas of psychological research. This dissertation, therefore, explores the insights that smartphone sensing reveals for psychological research using two examples, situation and affect research, making a twofold research contribution. First, in two empirical studies, different data types of smartphone-sensed data, such as GPS or phone data, were combined with experience-sampled self-report, and classical questionnaire data to gain valuable insights into individual behavior, thinking, and feeling in everyday life. Second, predictive modeling techniques were applied to analyze the large, high-dimensional data sets collected by smartphones. To gain a deeper understanding of the smartphone data, interpretable variables were extracted from the raw sensing data, and the predictive performance of various machine learning algorithms was compared. In summary, the empirical findings suggest that smartphone data can effectively capture certain situational and behavioral indicators of psychological phenomena in everyday life. However, in certain research areas such as affect research, smartphone data should only complement, but not completely replace, traditional questionnaire-based data as well as other data sources such as neurophysiological indicators. The dissertation also concludes that the use of smartphone sensor data introduces new difficulties and challenges for psychological research and that traditional methods and perspectives are reaching their limits. The complexity of data collection, processing, and analysis requires established guidelines for study design, interdisciplinary collaboration, and theory-driven research that integrates explanatory and predictive approaches. Accordingly, further research is needed on how machine learning models and other big data methods in psychology can be reconciled with traditional theoretical approaches. Only in this way can we move closer to the ultimate goal of psychology to better understand, explain, and predict human behavior and experiences and their interplay with everyday situations