Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases -- but translating academic theory into practice is inherently challenging. RS practitioners must balance the competing interests of diverse stakeholders, including providers and users, and operate in dynamic environments. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with other (legal, data, and fairness) teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, particularly when navigating multi-stakeholder and dynamic fairness considerations. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including HCI researchers and practitioners.

Spoken dialogue systems increasingly employ large language models (LLMs) to leverage their advanced reasoning capabilities. However, direct application of LLMs in spoken communication often yield suboptimal results due to mismatches between optimal textual and verbal delivery. While existing approaches adapt LLMs to produce speech-friendly outputs, their impact on reasoning performance remains underexplored. In this work, we propose Think-Verbalize-Speak, a framework that decouples reasoning from spoken delivery to preserve the full reasoning capacity of LLMs. Central to our method is verbalizing, an intermediate step that translates thoughts into natural, speech-ready text. We also introduce ReVerT, a latency-efficient verbalizer based on incremental and asynchronous summarization. Experiments across multiple benchmarks show that our method enhances speech naturalness and conciseness with minimal impact on reasoning. The project page with the dataset and the source code is available at https://yhytoto12.github.io/TVS-ReVerT

AI co-writing systems challenge long held ideals about agency and ownership in the creative process, thereby hindering widespread adoption. In order to address this, we investigate conceptions of agency and ownership in AI creative co-writing. Drawing on insights from a review of commercial systems, we developed three co-writing systems with identical functionality but distinct interface metaphors: agentic, tool-like, and magical. Through interviews with professional and non-professional writers (n = 18), we explored how these metaphors influenced participants' sense of control and authorship. Our analysis resulted in a taxonomy of agency and ownership subtypes and underscore how tool-like metaphors shift writers' expected points of control while agentic metaphors foreground conceptual contributions. We argue that interface metaphors not only guide expectations of control but also frame conceptions of authorship. We conclude with recommendations for the design of AI co-writing systems, emphasizing how metaphor shapes user experience and creative practice.

Peer recovery narratives provide unique benefits beyond professional or lay mentoring by fostering hope and sustained recovery in eating disorder (ED) contexts. Yet, such support is limited by the scarcity of peer-involved programs and potential drawbacks on recovered peers, including relapse risk. To address this, we designed RecoveryTeller, a chatbot adopting a recovered-peer persona that portrays itself as someone recovered from an ED. We examined whether such a persona can reproduce the support affordances of peer recovery narratives. We compared RecoveryTeller with a lay-mentor persona chatbot offering similar guidance but without a recovery background. We conducted a 20-day cross-over deployment study with 26 ED participants, each using both chatbots for 10 days. RecoveryTeller elicited stronger emotional resonance than a lay-mentor chatbot, yet tensions between emotional and epistemic trust led participants to view the two personas as complementary rather than substitutes. We provide design implications for mental health chatbot persona design.

Wealthy enclave sees a spate of'dangerous' incidents that have left residents terrified Charlie Kirk suspect's trans lover had a flirty relationship with ANOTHER man: Family and friends reveal couple's sick web of darkness What IS causing your maddening itch, should you scratch it... and when do you need to see a doctor? Fury as beach town eyes becoming new Vegas: Thousands of residents fear'predatory' casino will erase history Jimmy Kimmel was just the start. America is undergoing a tectonic shift. And the liberal Left, sick beyond help, are totally unprepared for what's coming next: MAUREEN CALLAHAN The sick moment I knew the'Zombie Hunter' was the sadistic serial killer I was looking for William's had a rotten 18 months. Many have remarked he's appeared unusually sombre - and not just when standing next to Prince Andrew.

The annual award ceremony features miniature operas, scientific demos, and 24/7 lectures. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Does alcohol enhance one's foreign language fluency? Do West African lizards have a preferred pizza topping? And can painting cows with zebra stripes help repel biting flies? These and other unusual research questions were honored tonight in a virtual ceremony to announce the 2025 recipients of the annual Ig Nobel Prizes.

The annual awards celebrate achievements that make us'laugh then think.' Breakthroughs, discoveries, and DIY tips sent every weekday. In the weeks before the Nobel Prizes are announced, the scientific community gathers every year for something a little more lighthearted: The Ig Nobel Prizes. Awarded to "honor achievements so surprising that they make people LAUGH, then THINK," this year marks the 35th anniversary of the awards. These prestigious awards celebrate science's more unusual contributions, honor the imaginative, and perhaps most importantly, spur people's interest in science, medicine, and technology . This year's honorees brought us pizza-eating lizards, tipsy bats, nail growth, and more that all celebrate the joy and fun in asking any and all questions.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'I just wanted to help.' Father turns to 9-year-old son for lifesaving stem cell donation Stephen Mondek became what Cedars-Sinai Medical Center believes is its youngest known stem cell donor. His father was dying of acute myeloid leukemia, a cancer that affects blood-forming cells in the bone marrow, and needed a donation to rebuild his immune system. This is read by an automated voice. Please report any issues or inconsistencies here .

This is the focus of work by and, which won the best paper award at the recent RoboCup symposium . The symposium takes place alongside the annual RoboCup competition, which this year was held in Salvador, Brazil. We caught up with some of the authors to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition. Could you start by giving us a brief description of the problem that you were trying to solve in your paper "Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots"? The main challenge we faced was that deep learning generally requires a large amount of labeled data. This is not a major problem for common tasks that have already been studied, because you can usually find labeled datasets online.

Capturing professionals' decision-making in creative workflows is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present CLEAR framework that structures reasoning into cognitive decision steps-linked units of actions, artifacts, and self-explanations that make decisions traceable. Building on this framework, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales to ease the knowledge-sharing burden. In a study with twelve creative professionals, 85% of ClearFairy's inferred rationales were accepted, increasing strong explanations from 14% to over 83% of decision steps without adding cognitive demand. The captured steps also enhanced generative AI agents in Figma, yielding next-action predictions better aligned with professionals and producing more coherent design outcomes. For future research on human knowledge-grounded creative AI agents, we release a dataset of captured 417 decision steps.