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Purpose: The purpose of this paper is to identify workforce skills that protect an occupation from elimination due to automation technology. Design/methodology/approach: The authors apply a Gaussian process (GP) classifier, based on the level of non-automatable work activities in an occupation, to USA and Colombian occupational datasets. Findings: The authors find that communication, interpersonal relationship management and decision-making skills are most important in occupations that are resistant to automation. Research limitations/implications: The results are based on work activities data from the Occupational Information Network (O*NET) database developed for the USA labor market. This dataset does not capture significant differences in work activities, where they exist, for the same occupation between the two countries. The findings are also limited to Colombia. Readers should be careful to extrapolate the findings outside of this geography. Originality/value: The authors discover that automation is likely to be a global phenomenon that can only be slightly mitigated by cultural and political factors.

Wage gaps between "skilled" and "unskilled" earners in the United States are conventionally attributed to U.S. workers' "skill deficits," or lack of skills necessary to deal with technological change, particularly automation. This paper argues instead that skills deficit/automation claims have always been a weak explanation for post-1979 trends. Since the mid-1990s all indications are that there is no basis for considering automation to be a significant factor in wage suppression or the growth of wage inequality. Rather, inequality growth has been an outgrowth of developments in the labor market rather than product markets. The key dynamic undercutting the typical worker's wage growth has been the strengthening of employers' power relative to their white-collar and blue-collar workers. The cause has not been monopoly firms exercising their power in product markets by charging higher prices to consumers. Monopolization has indeed contributed to wage suppression, but even this factor has largely run through the labor market, as monopoly firms squeezed supplier chain firms which in turn undercut their own workers' wages while seeing a profit squeeze as well. The paper concludes that what is needed is a better balancing of power in the labor market rather than a perfecting of competition. [ABSTRACT FROM AUTHOR]

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

This paper explores how professional engineers recognize and make sense of product defects in their everyday work. Such activities form a crucial, if often overlooked, part of professional engineering practice. By detecting, recognizing and repairing defects, engineers contribute to the creation of value and the optimization of production processes. Focusing on early-career engineers in an advanced steel mill in the United States, we demonstrate how learning specific ways of seeing and attending to defects take shape around the increasing automation of certain aspects of engineering work. Practices of sensing defects are embodied, necessitating disciplined eyes, ears, and hands, but they are also distributed across human and non-human actors. We argue that such an approach to technical work provides texture to the stark opposition between human and machine work that has emerged in debates around automation. Our approach to sensing defects suggests that such an opposition, with its focus on job loss or retention, misses the more nuanced ways in which humans and machines are conjoined in perceptual tasks. The effects of automation should be understood through such shifting configurations and the ways that they variously incorporate the perceptual practices of humans and machines. [ABSTRACT FROM AUTHOR]

Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading‐edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5‐m blade section, and the results were analyzed in terms of operation speed and accuracy. Finally, future work is discussed to improve the performance of the system. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED. [ABSTRACT FROM AUTHOR]

The decline of manufacturing employment is frequently invoked as a key cause of worsening U.S. population health trends, including rising mortality due to "deaths of despair." Increasing automation-- the use of industrial robots to perform tasks previously done by human workers-- is one structural force driving the decline of manufacturing jobs and wages. In this study, we examine the impact of automation on age- and sex-specific mortality. Using exogenous variation in automation to support causal inference, we find that increases in automation over the period 1993-2007 led to substantive increases in all- cause mortality for both men and women aged 45-54. Disaggregating by cause, we find evidence that automation is associated with increases in drug overdose deaths, suicide, homicide, and cardiovascular mortality, although patterns differ by age and sex. We further examine heterogeneity in effects by safety net program generosity, labor market policies, and the supply of prescription opioids. [ABSTRACT FROM AUTHOR]

I argue that the contemporary organization of professional work indicates the limits of answering typical questions about automation and technology in work to understand the practices of experts. Examining the case of automation in medicine, specifically Stereotaxis technology, I analyze a colligation of data on decisions about the uptake of this robotic technology among United States surgeons. I examine data including: procedure efficiency over time, the technology's affordances for the preservation of surgeons' bodily capital, changes in the profession's demographic profile, physician social networks, and clinical researchers' published assessments. The data suggest that automation of a central task can support and enhance the work of individual experts. They also show that using robotics does not improve efficiency. This case thus challenges the assumption that automation displaces work, at least in the case of professionals. And so, because Stereotaxis becomes more of a complement to surgeons' work as opposed to a substitute, this case points towards the importance of focusing attention less on job automation than on task automation. The case also highlights that users of the technology (physicians) value it differently than do purchasers (administrators). In addition, it identifies the differing considerations motivating their decisions to adopt or not adopt the technology. And in light of the finding that robotics use is more common among the rank-and-file than the elite, it may be that some professionals perceive automation to afford mobility opportunities. Based on these findings I propose new questions for scholarship on medicine, work, and automation, including those around "expert" versus "unskilled" labor, the body, and workplace divisions of labor. [ABSTRACT FROM AUTHOR]

The rationalization of human life in work, feeling, and relationships is amplified by artificial intelligence (AI), apps and automation, challenging interpersonal workers not only in how and whether they do their work, but also how they understand themselves as human. Given these trends, how do interpersonal workers interpret the humanness of their work? To answer this question, I focus on the interactive service work I call "connective labor," relying on 80+ in-depth interviews and 300+ hours of ethnographic observations with teachers, therapists and primary care physicians in the San Francisco Bay Area and mid-Atlantic United States, as well as with less advantaged practitioners such as sex workers, hairdressers, and home health care aides. I found that these interpersonal workers differentiated themselves from AI, automated agents, and robots in three ways: (1) by describing and defending their work as not rote, (2) taking pains to prove that they were not robots, and (3) justifying their judgments as safe, unique, and worthwhile. Much of their case rested on the unpredictability of humans, in terms of feelings, secrets, and mistakes. These findings have implications for race, class, and gender inequality, as advantage shaped how people were able to demand, perform, or experience their humanness in the ways that the proliferation of algorithmic technologies made salient. [ABSTRACT FROM AUTHOR]

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production. [ABSTRACT FROM AUTHOR]

The last decades have witnessed a revolution in manufacturing production characterized by increasing technology adoption and a strong expansion of international trade. Simultaneously, the income distribution has exhibited both polarization and concentration among the richest. Combining datasets from the U.S. Census Bureau, the U.S. Internal Revenue Service, the International Federation of Robotics, EU KLEMS, and COMTRADE, we study the causal effect of industrial automation on income inequality in the U.S. during 2010-2015. We exploit spatial and time variations in exposure to robots arising from past differences in industry specialization across U.S. metropolitan areas and the evolution of robot adoption across industries. We document a robust positive impact of robotics on income for only the top 1 percent of taxpayers, which is largest for top income fractiles. Therefore, industrial automation fuels income inequality and, particularly, top income inequality. According to our estimates, one more robot per thousand workers results in relative increments of the total taxable income accruing to fractiles P99 to P99.9, P99.9 to P99.99 and P99.99 to P100, of 2.1 percent, 3.5 percent and 5.9 percent, respectively. We also find that robotization leads to increased exports to high-income and upper-middle-income economies, and that this is one of the key mechanisms behind the surge in top income inequality. [ABSTRACT FROM AUTHOR]

Background: Fitness technologies such as wearables and sit-stand desks are increasingly being used to fight sedentary lifestyles by encouraging physical activity. However, adherence to such technologies decreases over time because of apathy and increased dismissal of behavioral nudges. Objective: To address this problem, we introduced shared autonomy in the context of sit-stand desks, where user input is integrated with robot autonomy to control the desk and reduce sedentary behavior and investigated user reactions and preferences for levels of automation with a sit-stand desk. As demographics affect user acceptance of robotic technology, we also studied how perceptions of nonvolitional behavior change differ across cultures (United States and India), sex, familiarity, dispositional factors, and health priming messages. Methods: We conducted a web-based vignette study in the United States and India where a total of 279 participants watched video vignettes of a person interacting with sit-stand desks of various levels of automation and answered questions about their perceptions of the desks such as ranking of the different levels of automation. Results: Participants generally preferred either manual or semiautonomous desks over the fully autonomous option (P<.001). However, participants in India were generally more amenable to the idea of nonvolitional interventions from the desk than participants in the United States (P<.001). Male participants had a stronger desire for having control over the desk than female participants (P=.01). Participants who were more familiar with sit-stand desks were more likely to adopt autonomous sit-stand desks (P=.001). No effects of health priming messages were observed. We estimated the projected health outcome by combining ranking data and hazard ratios from previous work and found that the semiautonomous desk led to the highest projected health outcome. Conclusions: These results suggest that the shared autonomy desk is the optimal level of automation in terms of both user preferences and estimated projected health outcomes. Demographics such as culture and sex had significant effects on how receptive users were to autonomous intervention. As familiarity improves the likelihood of adoption, we propose a gradual behavior change intervention to increase acceptance and adherence, especially for populations with a high desire for control. [ABSTRACT FROM AUTHOR]

Background: Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option. Objective: This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation. Methods: A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. Results: AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing. Conclusions: AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. [ABSTRACT FROM AUTHOR]

People around the world who seek to interact with large organisations increasingly find they must do so via mediated and automated communication. Organisations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people's satisfaction with these systems, we conducted a representative U.S. national survey (N = 1321). We found that people overwhelmingly like and trust in-person customer service compared to mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. However, communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide. [ABSTRACT FROM AUTHOR]

The present study, addresses a research question on the perception of fairness of job losses due to automation technologies amongst general stakeholders. Jennifer Kim (2018) has shown that job displacement due to automation is perceived to be fairer in the United States when compared to outsourcing. The study by Jennifer Kim (2018) is replicated in this study on a sample drawn from the population of the Indian labour force, limiting it to the millennials and the IT industry. The results indicated that all job displacement is perceived to be unfair, despite the motive behind it. This is contrary to the results obtained from the sample from the United States. Also, there was no statistically significant difference between the perception of fairness towards the job displacement due to automation and outsourcing. Though there was no direct effect of the motive behind the job displacement/lay-off on the perception of fairness, it was found that there were significant indirect effects from the identified 4 mediators. [ABSTRACT FROM AUTHOR]