ABSTRACT
Smart speakers have become pervasive in family homes, cre-ating the potential for these devices to influence parent-child dynamics and parenting behaviors. We investigate the im-pact of introducing a smart speaker to 10 families with chil-dren, over four weeks. We use pre- and post- deployment in-terviews with the whole family and in-home audio capture of parent-child interactions with the smart speaker for our anal-ysis. Despite the smart speaker causing occasional conflict in the home, we observed that parents leveraged the smart speaker to further parenting goals. We found three forms of influence the smart speaker has on family dynamics: 1) fos-tering communication, 2) disrupting access, and 3) augment-ing parenting. All of these influences arise from a commu-nally accessible, stand-alone voice interface which democra-tizes family access to technology. We discuss design impli-cations in furthering parenting practices and behaviors as the capabilities of the technology continue to improve.

ABSTRACT
Current approaches to AI and Assistive Technology (AT) often foreground task completion over other encounters such as expressions of care. Our paper challenges and complements such task-completion approaches by attending to the care work of access—the continual affective and emotional adjustments that people make by noticing and attending to one another. We explore how this work impacts encounters among people with and without vision impairments who complete tasks together. We find that bound up in attempts to get things done are concerns for one another and how well people are doing together. Reading this work through emerging disability studies and feminist STS scholarship, we account for two important forms of work that give rise to access: (1) mundane attunements and (2) non-innocent authorizations. Together these processes work as sensitizing concepts to help HCI scholars account for the ways that intelligent ATs both produce access while sometimes subverting people with disabilities.

ABSTRACT
Leveraging existing popular games such as Pokémon GO to promote health can engage people in healthy activities without sacrificing gaming appeal. However, little is known about what potential tensions arise from incorporating new healthrelated features to already existing and popular games and how to resolve those tensions from players’ perspectives. In this paper, we identify design tensions surrounding the appeals of Pokémon GO, perspectives on different health needs, and mobile health technologies. By conducting surveys and design workshops with 20 avid Pokémon GO players, we demonstrate four design tensions: (1) diverse goals and rewards vs. data accuracy, (2) strong bonds between players and characters vs. gaming obsession, (3) collaborative play vs. social anxiety, and (4) connection of in-real-life experiences with the game vs. different individual contexts. We provide design implications to resolve these tensions in Pokémon GO and discuss how to extend our findings to the broader context of health promotion in location-based games.

ABSTRACT
Beyond being the world’s largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing. To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys. Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of ‘distraction’.

ABSTRACT
Users are fundamental to HCI. However, little is known about how HCI education introduces students to working with users, particularly those different from themselves. To better understand design students’ engagement, reactions, and reflections with users, we investigate a case study of a graduate-level 10-week prototyping studio course that partnered with a children’s co-design team. HCI students participated in two co-design sessions with children to design a STEM learning experience for youth. We conducted participant observations, interviews with 14 students, and analyzed final artifacts. Our findings demonstrate the communication challenges and strategies students experienced, how students observed issues of power dynamics, and students’ perceived value in engaging with users. We contribute empirical evidence of how HCI students directly interact with target users, principles for reflective HCI pedagogy, and highlight the need for more intentional investigation into HCI educational practice.

ABSTRACT
Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates how explanations shape users’ perceptions of ML models with or without the ability to provide feedback to them: (1) does revealing model flaws increase users’ desire to “fix” them; (2) does providing explanations cause users to believe—wrongly—that models are introspective, and will thus improve over time. Through two controlled experiments—varying model quality—we show how the combination of explanations and user feedback impacted perceptions, such as frustration and expectations of model improvement. Explanations without opportunity for feedback were frustrating with a lower quality model, while interactions between explanation and feedback for the higher quality model suggest that detailed feedback should not be requested without explanation. Users expected model correction, regardless of whether they provided feedback or received explanations.