Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative research practices, we describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding, discussing, and disclosing the discretionary choices and subjectivity inherent to data science work. We first introduce the concepts of model positionality and computational reflexivity that can help data scientists to reflect on and communicate the social and cultural context of a model’s development and use, the data annotators and their annotations, and the data scientists themselves. We then describe the unique challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions. Finally, we demonstrate these techniques in a case study of the development of classifiers for toxic commenting in online communities.

While the HCI field increasingly examines how digital tools can support individuals in managing mental health conditions, it remains unclear how these tools can accommodate these conditions' temporal aspects. Based on weekly interviews with five individuals with depression, conducted over six weeks, this study identifies design opportunities and challenges related to extending technology-based support across fluctuating symptoms. Our findings suggest that participants perceive events and contexts in daily life to have marked impact on their symptoms. Results also illustrate that ebbs and flows in symptoms profoundly affect how individuals practice depression self-management. While digital tools often aim to reach individuals while they feel depressed, we suggest they should also engage individuals when they are less symptomatic, leveraging their energy and motivation to build habits, establish plans and goals, and generate and organize content to prepare for symptom onset.

Work breaks can play an important role in the mental and physical well-being of workers and contribute positively to productivity. In this paper we explore the use of activity-, physiological-, and indoor-location sensing to promote mobility during work-breaks. While the popularity of devices and applications to promote physical activity is growing, prior research highlights important constraints when designing for the workplace. With these constraints in mind, we developed BreakSense, a mobile application that uses a Bluetooth beacon infrastructure, a smartphone and a smartwatch to encourage mobility during breaks with a game-like design. We discuss constraints imposed by design for work and the workplace, and highlight challenges associated with the use of noisy sensors and methods to overcome them. We then describe a short deployment of BreakSense within our lab that examined bound vs. unbound augmented breaks and how they affect users' sense of completion and readiness to work.

Citizen science and communitysensing applications allow everyday citizens to collect data about the physical world to benefit science and society. Yet despite successes, current approaches are still limited by the number of domain-interested volunteers who are willing and able to contribute useful data. In this paper we introduce habitsourcing, an alternative approach that harnesses the habit-building practices of millions of people to collect environmental data. To support the design and development of habitsourcing apps, we present (1) interaction techniques and design principles for sensing through actuation, a method for acquiring sensing data from cued interactions; and (2) ExperienceKit, an iOS library that makes it easy for developers to build and test habitsourcing applications. In two experiments, we show that our two proof-of-concept apps, ZenWalk and Zombies Interactive, compare favorably to their non-data collecting counterparts, and that we can effectively extract environmental data using simple detection techniques.