Are workers' worries warranted? A comprehensive study guages AI implementation. - Psychology Today
<a href="https://news.google.com/rss/articles/CBMiowFBVV95cUxNdkNvY0JwYlFqNHJuZWZKeG5GM0hBRlZFc3ZPWnl5YUd3WmpXb28xc3FfR014WV96akRuZVJNeXh6UGVuZkRNeFJVd3AwSjV5YmpDY3lZeDUwOUFZdERrTnR2bUtKTUkzSjM3QV82Q2hFd2lyZWhZN0N0cUQ1NEwtNG8tRXRpM3pic1k4b1ppQndhVFlYQndHOUVERnFHNnF1cm1Z0gGoAUFVX3lxTE4zS0NNTWwyTXdja2wwV0xSbEhXbDVXNE5jTk0tMkRqQzF6ZHQ1eWJLYzhlYlhnbTVIYWtBN1B0blRWcUg3ejhsazVTUmRGVi1jVGlacDhjTUFSdE8yVmNVM29ETVZ6cFZmNFpKcjhZRmdZbG9MMGxVODJDZmxLcjZ0Mm9idjU4eTJnalVib0lmLXFxeTdKVWIzRGxtbXhnMHdNZjhWYy1yaA?oc=5" target="_blank">Are workers' worries warranted? A comprehensive study guages AI implementation.</a> <font color="#6f6f6f">Psychology Today</font>
Could not retrieve the full article text.
Read on GNews AI UK →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
study
Locating Risk: Task Designers and the Challenge of Risk Disclosure in RAI Content Work
arXiv:2505.24246v4 Announce Type: replace Abstract: As AI systems are increasingly tested and deployed in open-ended and high-stakes domains, crowdworkers are often tasked with responsible AI (RAI) content work. These tasks include labeling violent content, moderating disturbing text, or simulating harmful behavior for red teaming exercises to shape AI system behaviors. While prior research efforts have highlighted the risks to worker well-being associated with RAI content work, far less attention has been paid to how these risks are communicated to workers by task designers or individuals who design and post RAI tasks. Existing transparency frameworks and guidelines, such as model cards, datasheets, and crowdworksheets, focus on documenting model information and dataset collection process
AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes
arXiv:2603.21551v2 Announce Type: replace Abstract: Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validated competition designs and evaluation practices that remain fair and evidence-based. This paper presents a cross-regional study of LLM-centered Capture-the-Flag competitions built on the Cyber Security Awareness Week competition system. To understand how autonomy levels and participants' knowledge backgrounds influence problem-solving performance and learning-related behaviors, we formalize three autonomy levels: human-in-the-loop, autonomous agent frameworks, and hybrid. To enable verification, we require traceable submissions including conversation logs, agent
Togedule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability
arXiv:2505.01000v5 Announce Type: replace Abstract: Scheduling is a perennial-and often challenging-problem for many groups. Existing tools are mostly static, showing an identical set of choices to everyone, regardless of the current status of attendees' inputs and preferences. In this paper, we propose Togedule, an adaptive scheduling tool that uses large language models to dynamically adjust the pool of choices and their presentation format. With the initial prototype, we conducted a formative study (N=10) and identified the potential benefits and risks of such an adaptive scheduling tool. Then, after enhancing the system, we conducted two controlled experiments, one each for attendees and organizers (total N=66). For each experiment, we compared scheduling with verbal messages, shared c
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Analyst News
Balancing Efficiency and Empathy: Healthcare Providers' Perspectives on AI-Supported Workflows for Serious Illness Conversations in the Emergency Department
arXiv:2506.00241v2 Announce Type: replace Abstract: Serious Illness Conversations (SICs), discussions about values and care preferences for patients with life-threatening illness, rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for technology support, with a focus on AI. Our analysis revealed a four-stage SIC workflow (identification, preparation, conduction, documentation) and barriers at each stage, including fragmented patient information, limited time and space, lack of conversational guidance, and burdensome documentation. Providers expressed interest in AI systems for synthesizing information, supporting re
Machine learning-based prediction of peptide aggregation during chemical synthesis - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE9faks0dVd5LUczVlVKdGQ1LVV0cTRhZl9ENFJvclhnaDJTMW5NcHpTdmxudk81WThfN0ctc0Q4NUdaUDVkQTNHVzBGSmZMTWxuZTN5VmNfeHhIR1pPUmV3?oc=5" target="_blank">Machine learning-based prediction of peptide aggregation during chemical synthesis</a> <font color="#6f6f6f">Nature</font>
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!