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The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine-learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to the architecture's simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Using data from previous observing runs, we evaluate different fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. Our new O4 classifier shows improved performance, and will enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena. 

Introduction. Deskilling is a long-standing prediction of the use of information technology, raised anew by the increased capabilities of AI (AI) systems. A review of studies of AI applications suggests that deskilling (or levelling of ability) is a common outcome, but systems can also require new skills, i.e., upskilling. Method. To identify which settings are more likely to yield deskilling vs. upskilling, we propose a model of a human interacting with an AI system for a task. The model highlights the possibility for a worker to develop and exhibit (or not) skills in prompting for, and evaluation and editing of system output, thus yielding upskilling or deskilling. Findings. We illustrate these model-predicted effects on work with examples of current studies of AI-based systems. Conclusions. We discuss organizational implications of systems that deskill or upskill workers and suggest future research directions. 

The recent addition of data journalists to several dozen U.S. public radio newsrooms has created multiple new hybridities in the form. No longer are numbers and large datasets “audio poison.” Instead, they are an essential tool for these journalists, who prize journalism’s interpretive function, expressing information in new ways and challenging conventions of broadcast newsroom employment. This study, which relies on semi-structured interviews with 13 public radio data journalists, uses Carlson’s boundary work typology to analyze the ways in which data journalists are expanding the boundaries of U.S. public radio journalism, as well as ways in which they have pushed back against expulsionary pressures. This study’s findings problematize the idea that the results of boundary work must be expressed as in-or-out proposition. Rather, U.S. public radio data journalists suggest their boundaries are a continuum where they may be conditionally accepted by their colleagues, depending on deadlines and on the skills possessed by non-data journalists. 

Artificial Intelligence (AI) and citizen science (CS) are two approaches to tackling data challenges related to scale and complexity. CS by its very definition relies on the joint effort of typically a distributed group of non-expert people to solve problems in a manner that relies on human intelligence. As AI capabilities increasingly augment or complement human intelligence, if not replicate it, there is a growing effort to understand the role that AI can play in CS and vice versa. With this growing interest as context, this special collection, The Future of AI and Citizen Science, illustrates the many ways that CS practitioners are integrating AI into their efforts, as well as identifies current limitations. In this spirit, our editorial briefly introduces the special collection papers to demonstrate and assess some uses of AI in CS; then, we contextualize these uses in terms of key challenges; and conclude with future directions that use AI with CS in both innovative and ethical ways. 

PurposeResearch on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media. Design/methodology/approachWe investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment. FindingsDifferent interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity. Originality/valueFindings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines. 

In response to the COVID-19 crisis, many local television (TV) newsrooms decided to have employees work from home (WFH) or from the field rather than from the newsroom, creating a kind of hybrid work characterized by a mix of work locations. From a review of research on telework and WFH, we identified possible impacts of WFH on work and on workers, with a particular focus on news work and news workers. Data on the impacts of hybrid work are drawn from interviews with local television news directors and journalists in the United States and observations of WFH. We found that through the creative application of technology, WFH news workers could successfully create a newscast, albeit with some concerns about story quality. However, WFH did not seem to satisfy workers’ needs for socialization or learning individually or as a group and created some problems coordinating work. Lifted restrictions on gatherings have mitigated some of the experienced problems, but we expect to see continued challenges to news workers’ informal learning in hybrid work settings. 

We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.