While Alexa can perform over 100,000 skills, its capability covers only a fraction of what is possible on the web. Individuals need and want to automate a long tail of web-based tasks which often involve visiting different websites and require programming concepts such as function composition, conditional, and iterative evaluation. This paper presents DIYA (Do-It-Yourself Assistant), a new system that empowers users to create personalized web-based virtual assistant skills that require the full generality of composable control constructs, without having to learn a formal programming language.

Internet companies track users' online activity to make inferences about their interests, which are then used to target ads and personalize their web experience. Prior work has shown that existing privacy-protective tools give users only a limited understanding and incomplete picture of online tracking. We present Tracking Transparency, a privacy-preserving browser extension that visualizes examples of long-term, longitudinal information that third-party trackers could have inferred from users' browsing. The extension uses a client-side topic modeling algorithm to categorize pages that users visit and combines this with data about the web trackers encountered over time to create these visualizations. We conduct a longitudinal field study in which 425 participants use one of six variants of our extension for a week. We find that, after using the extension, participants have more accurate perceptions of the extent of tracking and also intend to take privacy-protecting actions.

Much of what a user sees browsing the internet, from ads to search results, is targeted or personalized by algorithms that have made inferences about that user. Prior work has documented that users find such targeting simultaneously useful and creepy. We begin unpacking these conflicted feelings through two online studies. In the first study, 306 participants saw one of ten explanations for why they received an ad, reflecting prevalent methods of targeting based on demographics, interests, and other factors. The type of interest-based targeting described in the explanation affected participants' comfort with the targeting and perceptions of its usefulness. We conducted a follow-up study in which 237 participants saw ten interests companies might infer. Both the sensitivity of the interest category and participants' actual interest in that topic significantly impacted their attitudes toward inferencing. Our results inform the design of transparency tools.