Michael S. Bernstein

Speakers constantly undergo audience understanding checks where their communication is predicated on an updated perception of the audience at hand. This ability to “read the room” is critical for successful presentations, but there are many possible “rooms” of audiences, and many ways a speaker may want to “read” their responses. Status quo interaction methods flatten these audience understanding checks onto one lens—i.e., via the first raised hands or the most-upvoted comments—but what if a PhD student instead wants to monitor excitement about lines of future work, an educator wants to prepare for areas of student confusion, or a researcher wants to examine critiques from external fields? To aid speakers in their unique goals, we introduce Refractor, a tool that supports speakers through audience understanding checks via speaker-specified text inputs called lenses . For example, a speaker can specify an “urgent questions” lens during a live presentation, and the system will display a high-level synthesis of time-critical audience misunderstandings. Then, after the talk, the speaker can apply an “interdisciplinary critiques” or “suggestions to expand work” lens to explore the audience’s perspectives on future work. To instantiate Refractor, we introduce Steerable Live Clustering (SLiC), an algorithm that uses speaker-specified lenses to rapidly prioritize and thematically group text comments at interactive speeds. Via a series of live deployments, we observe that participants used Refractor to pursue a myriad of directions for audience understanding checks, such as brainstorming new directions, prioritizing important feedback, and triaging relevant questions in a timely manner.

Social media feed algorithms rank content that is purported to be preferred by users, but the engagement behaviors that drive these algorithms are (at best) indirect proxies for users’ explicitly self-stated values. Are the resulting feeds value aligned, and if not, why? We investigate this question by annotating the basic human values expressed in participants’ X (Twitter) feeds (N = 715 US users), analyzing the relationship between the posts’ value expressions and the posts’ amplification in the ranked “For You” Page feed, and then comparing the amplified values to users’ own values. We observe that the inventory of posts from followed accounts reflects users’ self-stated values—but that there is an overall negative correlation (misalignment) between users’ explicit values and the value expressions the algorithm is more likely to amplify. We turn to engagement behavior to understand this misalignment and observe that users’ engagement behaviors can be misaligned with their stated values—likely causing the algorithm to learn and reflect these misaligned values. We also detect partisan differences consistent with this theory: While the algorithm amplifies values negatively correlated with both Democrats’ and Republicans’ self-stated values, they are more misaligned for Democrats. And in fact replying, a heavily weighted form of engagement, is associated with values that are less aligned for both Democrats’ and Republicans’ self-stated values, and is even more misaligned for Democrats. Taken together, these findings offer a glimpse into the tensions between the values that people hold and those that provoke reactions, and how these value tensions can produce misaligned outcomes.

Persistent self-criticism—harsh evaluative self-talk—can undermine illustrators’ performance and well-being. Traditional interventions draw on psychotherapeutic approaches (e.g., compassion training) but sit outside the illustration workflow, requiring time, facilitation, and skill transfer. We propose an in-workflow alternative: evaluative off-centering , a mechanism redirecting self-critical evaluation away from an inherently self-evaluative task (like illustration) by embedding it in an alternative activity. We instantiate evaluative off-centering in Art Card Game (ACG) that integrates illustration into a card customization game: players illustrate cards that become playable assets in a head-to-head battle. In a four-day randomized controlled study with hobbyist and professional illustrators (N = 38), ACG outperformed a control condition with identical illustration constraints but no evaluative off-centering mechanisms (e.g. multiplayer, gameplay), yielding significantly higher pride in produced artwork and activity enjoyment. Pride and enjoyment—positive affect states linked to lower self-criticism—help explain how ACG reduces self-criticism. We discuss design implications for creativity support tools that apply evaluative off-centering across creative domains.

Users trust algorithms more when they can predict the algorithms’ behavior. Simple algorithms trivially yield predictively accurate mental models, but modern AI algorithms have often been assumed too complex for people to build predictive mental models, especially in the social media domain. In this paper, we describe conditions under which even complex algorithms can yield predictive mental models, opening up opportunities for a broader set of human-centered algorithms. We theorize that users will form an accurate predictive mental model of an algorithm’s behavior if and only if the algorithm simultaneously satisfies three criteria: (1) cognitive availability of the underlying concepts being modeled, (2) concept compactness (does it form a single cognitive construct?), and (3) high alignment between the person’s and algorithm’s execution of the concept. We evaluate this theory through a pre-registered experiment ( N = 1250) where users predict behavior of 25 social media feed ranking algorithms that vary on these criteria. We find that even complex (e.g., LLM-based) algorithms enjoy accurate prediction rates when they meet all criteria, and even simple (e.g., basic term count) algorithms fail to be predictable when a single criterion fails. We also find that these criteria determine outcomes beyond prediction accuracy, such as which mental models users deploy to make their predictions.

We often treat social media as a lens onto society. How might that lens distort the popularity of political and social viewpoints? We examine discrepancies between publicly posted and privately surveyed opinions within communities, contributing a measurement of the “spiral of silence” theory; the theory posits people are less likely to voice opinions when they believe they hold minority views, creating a reinforcing cycle where these opinions are expressed less. We surveyed members of politically-oriented Reddit communities about their willingness to post on contentious topics, yielding 439 responses across twelve subreddits. 72.1% of participants who perceive themselves in the minority remain silent and are half as likely to post compared to those who believe their opinion is in the majority. Community design factors, such as perceived diversity, are associated with less self-silencing. We provide recommendations for counteracting self-silencing at the community level (e.g., positive reinforcement, more transparent moderation). Overall, these results reveal gaps between online discourse and broader public opinion.

Large language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user’s in-the-moment objective, then rapidly optimizing for that singular objective, enables LLMs to produce specialized tools, interfaces, and responses. Our work introduces just-in-time objectives, which model a user’s goals to specialize LLM systems on the fly. We contribute an architecture for automatically inducing such objectives by passively observing user behavior, then steering downstream AI systems through generation and evaluation against this objective. Inducing just-in-time objectives (e.g., “Clarify the abstract’s research contribution”) enables automatic generation of tools, e.g., those that critique a draft based on relevant HCI methodologies, anticipate related researchers’ reactions, or surface ambiguous terminology. In a series of experiments on participants’ own tasks, JIT objectives enable LLM outputs that achieve 66–86% win rates over typical LLMs. In-person use sessions confirm that JIT objectives produce specialized tools that are unique to each participant and are rated as significantly higher quality than a standard LLM chat tool.