Targeted online advertising systems increasingly draw scrutiny for the surveillance underpinning their collection of people’s private data, and subsequent automated categorization and inference. The experiences of LGBTQ+ people, whose identities call into question dominant assumptions about who is seen as “normal,” and deserving of privacy, autonomy, and the right to self-determination, are a fruitful site for exploring the impacts of ad targeting. We conducted semi-structured interviews with LGBTQ+ individuals (N=18) to understand their experiences with online advertising, their perceptions of ad targeting, and the interplay of these systems with their queerness and other identities. Our results reflect participants’ overall negative experiences with online ad content—they described it as stereotypical and tokenizing in its lack of diversity and nuance. But their desires for better ad content also clashed with their more fundamental distrust and rejection of the non-consensual and extractive nature of ad targeting. They voiced privacy concerns about continuous data aggregation and behavior tracking, a desire for greater control over their data and attention, and even the right to opt-out entirely. Drawing on scholarship from queer and feminist theory, we explore targeted ads’ homonormativity in their failure to represent multiply-marginalized queer people, the harms of automated inference and categorization to identity formation and self-determination, and the theory of refusal underlying participants’ queer visions for a better online experience.

Programmers often rely on online resources—such as code examples, documentation, blogs, and Q&A forums—to compare similar libraries and select the one most suitable for their own tasks and contexts. However, this comparison task is often done in an ad-hoc manner, which may result in suboptimal choices. Inspired by Analogical Learning and Variation Theory, we hypothesize that rendering many concept-annotated code examples from different libraries side-by-side can help programmers (1) develop a more comprehensive understanding of the libraries’ similarities and distinctions and (2) make more robust, appropriate library selections. We designed a novel interactive interface, ParaLib, and used it as a technical probe to explore to what extent many side-by-side concept-annotated examples can facilitate the library comparison and selection process. A within-subjects user study with 20 programmers shows that, when using ParaLib, participants made more consistent, suitable library selections and provided more comprehensive summaries of libraries’ similarities and differences.

Many programmers want to use deep learning due to its superior accuracy in many challenging domains. Yet our formative study with ten programmers indicated that, when constructing their own deep neural networks (DNNs), they often had a difficult time choosing appropriate model structures and hyperparameter values. This paper presents ExampleNet—a novel interactive visualization system for exploring common and uncommon design choices in a large collection of open-source DNN projects. ExampleNet provides a holistic view of the distribution over model structures and hyperparameter settings in the corpus of DNNs, so users can easily filter the corpus down to projects tackling similar tasks and compare and contrast design choices made by others. We evaluated ExampleNet in a within-subjects study with sixteen participants. Compared with the control condition (i.e., online search), participants using ExampleNet were able to inspect more online examples, make more data-driven design decisions, and make fewer design mistakes.