RESUMEN
Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and ed it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation. © 2023 Owner/Author.
RESUMEN
The COVID-19 pandemic has altered the working culture at various organizations;what began as a public health safety measure, remote work is continuing to reshape work in America and beyond. However, remote work has fared differently for different workers and for different organizations, contributing to better work-life balance for some, while increased burnout for others. What aspects of an organization's culture make it less or more favorable to remote work? We answer this question by creating, analyzing, and subsequently releasing a large dataset of employee reviews shared anonymously on Glassdoor. Adopting a worker-centered approach grounded in organizational culture theory, we extract organizational cultural factors salient in the language of employee reviews of 52 Fortune 500 companies. Through a prediction task, we identify what distinguishes companies perceived to be desirable for remote work versus others, noted in company rankings following the pandemic. Our dataset and findings can serve to be valuable evidence-base and resources for efforts to define a new future of work post-pandemic. © 2023 Owner/Author.
RESUMEN
Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member's recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member's memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making. © 2022 ACM.
RESUMEN
In light of the ongoing COVID-19 pandemic, remote work styles have become the norm. However, these work settings introduce new intricacies in worker behaviors. The overlap between work and home can disrupt performance. The lack of social interaction can affect motivation. This elicits a need to implement novel methods to evaluate and enhance remote worker functioning. The potential to unobtrusively and automatically assess such workers can be fulfilled by social media and ubiquitous technologies. This paper situates recent research in the new context by extending our insights for increased remote interaction and online presence. We present implications for proactive assessment of remote workers by understanding day-level activities, coordination, role awareness, and organizational culture. Additionally, we discuss the ethics of privacy-preserving deployment, employer surveillance, and digital inequity. This paper aims to inspire pervasive technologies for the new future of work. © 2020 IEEE.