AI agents are increasingly capable of autonomously executing complex, multi-step tasks. In the retail sector, this means consumers can now delegate their shopping to AI agents that browse assortments, evaluate tradeoffs, and even initiate checkout. With companies like OpenAI, Google, and Amazon deploying these tools at scale, the market impact of this technological shift is not yet understood. While humans face high search costs and rarely browse deeply—allowing top-ranked products to capture most sales—AI agents promise to lower search costs and enable more efficient product discovery. Because agents can effectively evaluate large assortments at near-zero marginal cost, classic intuition from search theory suggests that reducing search costs should decrease concentration: consumers (or their agents) should be more willing to explore and discover a wider set of products. Alternatively, AI agents, which are trained on historical corpora, may encode systematic priors or biases toward particular brands or products, which could instead increase concentration. We study whether delegating search to AI agents expands or concentrates demand, and through what mechanisms. To properly understand how AI agents may change user behaviors, we must first understand how humans currently conduct search on e-commerce platforms.

Free-text survey responses can provide nuance often missed by structured questions, but remain difficult to statistically analyze. To address this, we introduce In Your Own Words, a computational framework for exploratory analyses of free-text survey data that identifies structured, interpretable themes in free-text responses more precisely than previous computational approaches, facilitating systematic analysis. To illustrate the benefits of this approach, we apply it to free-text descriptions of race, gender, and sexual orientation from 1,004 U.S. participants. The themes our approach learns have three practical applications in survey research. First, the themes can suggest structured questions to add to future surveys by surfacing salient constructs—such as belonging and identity fluidity—that existing surveys do not capture. Second, the themes reveal heterogeneity within standardized categories, explaining additional variation in health, well-being, and identity importance. Third, the themes illuminate systematic discordance between self-identified and perceived identities, highlighting mechanisms of misrecognition that existing measures do not reflect. More broadly, our framework can be deployed in a wide range of survey settings to identify interpretable themes from free text, complementing existing qualitative methods.

Mainstream news organizations shape public perception not only directly through the articles they publish but also through the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. However, measuring these subtle forms of media bias at scale remains a challenge. Here, we introduce a large, ongoing (from January 1, 2024 to present), near real-time dataset and computational framework developed to enable systematic study of selection and framing bias in news coverage. Our pipeline integrates large language models (LLMs) with scalable, near-real-time news scraping to extract structured annotations—including political lean, tone, topics, article type, and major events—across hundreds of articles per day. We quantify these dimensions of coverage at multiple levels—the sentence level, the article level, and the publisher level—expanding the ways in which researchers can analyze media bias in the modern news landscape. In addition to a curated dataset, we also release an interactive web platform for convenient exploration of these data. Together, these contributions establish a reusable methodology for studying media bias at scale, providing empirical resources for future research. Leveraging the breadth of the corpus over time and across publishers, we also present some examples (focused on the 150,000+ articles examined in 2024) that illustrate how this novel data set can reveal insightful patterns in news coverage and bias, supporting academic research and real-world efforts to improve media accountability.

Mainstream media, through their decisions on what to cover and how to frame the stories they cover, can mislead readers without using outright falsehoods. Therefore, it is crucial to have tools that expose these editorial choices underlying media bias. In this paper, we introduce the Media Bias Detector, a tool for researchers, journalists, and news consumers. By integrating large language models, we provide near real-time granular insights into the topics, tone, political lean, and facts of news articles aggregated to the publisher level. We assessed the tool’s impact by interviewing 13 experts from journalism, communications, and political science, revealing key insights into usability and functionality, practical applications, and AI’s role in powering media bias tools. We explored this in more depth with a follow-up survey of 150 news consumers. This work highlights opportunities for AI-driven tools that empower users to critically engage with media content, particularly in politically charged environments.