Recent privacy laws have strengthened data subjects' right to access personal data collected by companies. Prior work has found that data exports companies provide consumers in response to Data Subject Access Requests (DSARs) can be overwhelming and hard to understand. To identify directions for improving the user experience of data exports, we conducted an online study in which 33 participants explored their own data from Amazon, Facebook, Google, Spotify, or Uber. Participants articulated questions they hoped to answer using the exports. They also annotated parts of the data they found confusing, creepy, interesting, or surprising. While participants hoped to learn either about their own usage of the platform or how the company collects and uses their personal data, these questions were often left unanswered. Participants' annotations documented their excitement at finding data records that triggered nostalgia, but also shock about the privacy implications of other data they saw. Having examined their data, many participants hoped to request the company erase some, but not all, of the data. We discuss opportunities for future transparency-enhancing tools and enhanced laws.

Current algorithmic fairness tools focus on auditing completed models, neglecting the potential downstream impacts of iterative decisions about cleaning data and training machine learning models. In response, we developed Retrograde, a JupyterLab environment extension for Python that generates real-time, contextual notifications for data scientists about decisions they are making regarding protected classes, proxy variables, missing data, and demographic differences in model performance. Our novel framework uses automated code analysis to trace data provenance in JupyterLab, enabling these notifications. In a between-subjects online experiment, 51 data scientists constructed loan-decision models with Retrograde providing notifications continuously throughout the process, only at the end, or never. Retrograde’s notifications successfully nudged participants to account for missing data, avoid using protected classes as predictors, minimize demographic differences in model performance, and exhibit healthy skepticism about their models.

To counteract the ads and third-party tracking ubiquitous on the web, users turn to blocking tools--ad-blocking and tracking-protection browser extensions and built-in features. Unfortunately, blocking tools can cause non-ad, nontracking elements of a website to degrade or fail, a phenomenon termed breakage . Examples include missing images, nonfunctional buttons, and pages failing to load. While the literature frequently discusses breakage, prior work has not systematically mapped and disambiguated the spectrum of user experiences subsumed under "breakage," nor sought to understand how users experience, prioritize, and attempt to fix breakage. We fill these gaps. First, through qualitative analysis of 18,932 extension-store reviews and GitHub issue reports for ten popular blocking tools, we developed novel taxonomies of 38 specific types of breakage and 15 associated mitigation strategies. To understand subjective experiences of breakage, we then conducted a 95-participant survey. Nearly all participants had experienced various types of breakage, and they employed an array of strategies of variable effectiveness in response to specific types of breakage in specific contexts. Unfortunately, participants rarely notified anyone who could fix the root causes. We discuss how our taxonomies and results can improve the comprehensiveness and prioritization of ongoing attempts to automatically detect and fix breakage.

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.

Research to Practice Full Paper: The reliance on undergraduate students to take on key aspects of college level courses, such as grading, holding office hours, and facilitating small group work, has been growing with the increased enrollment in many introductory STEM courses. There is an urgent need to understand how these undergraduate teaching assistants (UTAs) approach and engage their roles in the wake of the increasing dependency on these students to facilitate learning within courses. Over the past few years at Brown University, and many others, UTAs have been hired at a growing rate and have been expected to take on new responsibilities. However, institutional policies and guidelines have not changed to reflect these new conditions, raising the question as to what are points of tension in the UTA experience and what are the causes of such tensions. This paper aims to address this concern by identifying and analyzing these tensions that arise as UTAs work to be successful educators.To further understand the tensions and pressures experienced by these student workers, we interviewed UTAs in engineering, physics, computer science, and one UTA in engineering and computer science (n=5). The departments vary according to the size and scope of their respective UTA programs. We use the framework of Activity Systems Analysis (ASA) in an effort to identify areas of tension and improvement in current teaching and support practices. The ASA framework allows researchers to narrow their focus on human activity and experiences, in this case those of the UTA, while not losing sight of the cultural and historical contexts in which this activity and these experiences take place. The framework also allows for the identification of systemic tensions that tie human activities and environments to undesired outcomes at an individual or systemic level. We identify tensions from the UTA experiences, provide recommendations for ways to better support and develop STEM UTAs, and identify future areas of research.