Incorporating ethics into computing education has become a priority for the SIGCSE community. Many computing departments and educators have contributed to this endeavor by creating standalone computing ethics courses or integrating ethics modules and discussions into preexisting curricula. In this study, we hope to support this effort by reporting on computing educators' attitudes toward including ethics in their computing classroom, with a special focus on the structures that hinder or help this endeavor. We surveyed 138 higher education computing instructors to understand their attitudes toward including ethics in their classes, what barriers might be preventing them from doing so, and which structures best support them. We found that even though instructors were generally positive about ethics as a component of computing education, there are specific barriers preventing ethics from being included in some computing courses. In this work, we explore how to alleviate these barriers and outline support structures that could encourage further integration of ethics and computing in higher education.

Measuring and assessing the impact and “fairness’’ of recommendation algorithms is central to responsible recommendation efforts. However, the complexity of fairness definitions and the proliferation of fairness metrics in research literature have led to a complex decision-making space. This environment makes it challenging for practitioners to operationalize and pick metrics that work within their unique context. This suggests that practitioners require more decision-making support, but it is not clear what type of support would be beneficial. We conducted a literature review of 24 papers to gather metrics introduced by the research community for measuring fairness in recommendation and ranking systems. We organized these metrics into a ‘decision-tree style’ support framework designed to help practitioners scope fairness objectives and identify fairness metrics relevant to their recommendation domain and application context. To explore the feasibility of this approach, we conducted 15 semi-structured interviews using this framework to assess which challenges practitioners may face when scoping fairness objectives and metrics for their system, and which further support may be needed beyond such tools.

Though recommender systems fundamentally rely on human input and feedback, human-centered research in the RecSys discipline is lacking. When recommender systems aim to treat users more fairly, misinterpreting user objectives could lead to unintentional harm, whether or not fairness is part of the aim. When users seek to understand recommender systems better, a lack of transparency could act as an obstacle for their trust and adoption of the platform. Human-centered machine learning seeks to design systems that understand their users, while simultaneously designing systems that the users can understand. In this work, I propose to explore the intersection of transparency and user-system understanding through three phases of research that will result in a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems.

Despite increasing reliance on personalization in digital platforms, many algorithms that curate content or information for users have been met with resistance. When users feel dissatisfied or harmed by recommendations, this can lead users to hate, or feel negatively towards these personalized systems. Algorithmic hate detrimentally impacts both users and the system, and can result in various forms of algorithmic harm, or in extreme cases can lead to public protests against “the algorithm” in question. In this work, we summarize some of the most common causes of algorithmic hate and their negative consequences through various case studies of personalized recommender systems. We explore promising future directions for the RecSys research community that could help alleviate algorithmic hate and improve the relationship between recommender systems and their users.

Increasing attention to the role of ethical consideration in computing has led to calls for greater integration of this critical topic into technical classes rather than siloed in standalone computing ethics classes. The motivation for such integration is not only to support in-situ learning, but also to emphasize to students that ethical consideration is inherently part of the technical practice of computing. We propose that the logical place to begin emphasizing ethics is on day one of computing education: in introductory programming classes. This paper presents one approach to ethics integration into such classes: assignments that teach basic programming concepts (e.g., conditionals or iteration) but are contextualized with real-world ethical dilemmas or concepts. We report on experiences with this approach in multiple introductory programming courses, including details about select assignments, insights from instructors and teaching assistants, and results from surveys of a subset of students who took these courses. Based on these experiences we provide preliminary plans for future work, along with a roadmap for instructors to emulate our approach and suggestions for overcoming challenges they might face.

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features – informed by the needs of our participants – that could improve user understanding of and trust in fairness-aware recommender systems.

The workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics.