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People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We tested the efficacy of behavior descriptions through user studies with 225 participants in three distinct domains: fake review detection, satellite image classification, and bird classification. We found that behavior descriptions can increase human-AI accuracy through two mechanisms: helping people identify AI failures and increasing people's reliance on the AI when it is more accurate. These findings highlight the importance of people's mental models in human-AI collaboration and show that informing people of high-level AI behaviors can significantly improve AI-assisted decision making. 

Algorithmic systems help manage the governance of digital platforms featuring user-generated content, including how money is distributed to creators from the profits a platform earns from advertising on this content. However, creators producing content about disadvantaged populations have reported that these kinds of systems are biased, having associated their content with prohibited or unsafe content, leading to what creators believed were error-prone decisions to demonetize their videos. Motivated by these reports, we present the results of 20 interviews with YouTube creators and a content analysis of videos, tweets, and news about demonetization cases to understand YouTubers' perceptions of demonetization affecting videos featuring disadvantaged or vulnerable populations, as well as creator responses to demonetization, and what kinds of tools and infrastructure support they desired. We found creators had concerns about YouTube's algorithmic system stereotyping content featuring vulnerable demographics in harmful ways, for example by labeling it unsafe'' for children or families -- creators believed these demonetization errors led to a range of economic, social, and personal harms. To provide more context to these findings, we analyzed and report on the technique a few creators used to audit YouTube's algorithms to learn what could cause the demonetization of videos featuring LGBTQ people, culture and/or social issues. In response to the varying beliefs about the causes and harms of demonetization errors, we found our interviewees wanted more reliable information and statistics about demonetization cases and errors, more control over their content and advertising, and better economic security. 

In-app privacy notices can help smartphone users make informed privacy decisions. However, they are rarely used in real-world apps, since developers often lack the knowledge, time, and resources to design and implement them well. We present Honeysuckle, a programming tool that helps Android developers build in-app privacy notices using an annotation-based code generation approach facilitated by an IDE plugin, a build system plugin, and a library. We conducted a within-subjects study with 12 Android developers to evaluate Honeysuckle. Each participant was asked to implement privacy notices for two popular open-source apps using the Honeysuckle library as a baseline as well as the annotation-based approach. Our results show that the annotation-based approach helps developers accomplish the task faster with significantly lower cognitive load. Developers preferred the annotation-based approach over the library approach because it was much easier to learn and use and allowed developers to achieve various types of privacy notices using a unified code format, which can enhance code readability and benefit team collaboration. 

While most online accounts are designed assuming a single user, past work has found that romantic couples often share many accounts. Our study examines couples' account sharing behaviors as their relationships develop. We conducted 19 semi-structured interviews with people who are currently in romantic relationships to understand couples' account sharing behaviors over the lifecycle of their relationship. We find that account sharing behaviors progress through a relationship where major changes happen at the start of cohabitation, marriage, and occasional breakup. We also find that sharing behaviors and motivations are influenced by couples' relationship ecology, which consists of the dynamics between the couples and the social environment they live in. Based on these findings, we discuss implications for further study to support couples' sharing needs at different relationship stages and identify design opportunities for technology solutions to facilitate couples' sharing.