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1. Adaptive Task Allocation Preferences in Different Workload Scenarios in Driving Automation Systems

   https://doi.org/10.1177/1071181322661426  

   Taylor, Skye; Garcia, Katherine; Chen*, Jing; Hu, Bin (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Adaptive task allocation is used in many human-machine systems and has been proven to improve operators’ performance with automated systems. However, there has been limited knowledge surrounding the benefits of adaptive task allocation in automated vehicles. In this study, participants were presented with photos and videos depicting driving scenarios of low or high workloads at two levels of automation (SAE Levels 2 and 3). The participants reported which tasks they felt comfortable allocating to themselves or to the driving automation system (DAS) in each driving scenario, as well as whether they would conduct the task allocation manually or have the DAS automatically allocate the tasks. Our results showed that participants preferred conducting manual task allocation and preferred the system to complete more tasks when the perceived workload was high. There was no significant difference between the high and low workload scenarios in terms of whether participants chose to allocate tasks. 

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2. Impacts of the Use of Machine Learning on Work Design

   https://doi.org/10.1145/3406499.3415070  

   Crowston, Kevin; Bolici, Francesco (November 2020, 8th International Conference on Human-Agent Interaction)

   null (Ed.)

   The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML—decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms. 

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3. Automation and New Tasks: How Technology Displaces and Reinstates Labor

   https://doi.org/10.1257/jep.33.2.3  

   Acemoglu, Daron; Restrepo, Pascual (May 2019, Journal of Economic Perspectives)

   We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades. 

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4. Analyzing Expert Design Cost Estimation for Additive Manufacturing

   https://doi.org/10.1115/DETC2022-90260  

   Williams, Glen; Meisel, Nicholas A.; Simpson, Timothy W.; McComb, Christopher (August 2022, ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing design for additive manufacturing activity parameters. We then present a series of case study designs, with varied functionality and geometric complexity, to experts and measure their estimations of design labor for each case. Summary statistics of the cost estimates and a linear mixed effects model predicting labor responses from participant and design attributes was used to estimate the significance of factors on the responses. A task-based, CAD model complexity calculation is then used to infer an estimate of the magnitude and variability of normalized labor cost to understand more generalizable attributes of the observed labor estimates. These two analyses are discussed in the context of advantages and disadvantages of relying on human cost estimation for additive manufacturing forecasts as well as future work that can prioritize and mitigate such challenges. 

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5. Typology Development for Synthetic Chemistry Sub-Tasks: Towards Human-Robot Collaboration Task Design in the Wet Lab

   https://doi.org/10.1115/DETC2024-142677  

   Jung, Diane N; Bruns, Carson J (August 2024, American Society of Mechanical Engineers)

   Chemical manufacturing is a growing field that contributes to many industries and employs tens of thousands of researchers in wet labs. Automation tools for synthetic chemistry are of interest not only for their potential impact on efficiency and productivity, but also on human resources and safety, since synthetic chemistry poses a number of occupational risks and is largely inaccessible to researchers with physical disabilities. Currently, most automation tools for synthetic chemistry are either designed to perform highly specialized tasks or they are designed as closed-loop systems with minimal interaction between human and machine during a synthesis procedure. We are pursuing an alternative, human-centered approach to robotic tools for synthetic chemistry, in which general-purpose collaborative robots (cobots) offer diverse forms of support to human researchers in the lab. In order to design frameworks for productive scientist-cobot collaborations, we need a deep understanding of the task space in synthetic chemistry labs and the impact of these various activities on the researchers. Based on observations and surveys from a group of experimental scientists, we have identified and analyzed 10 manual tasks commonly performed by researchers in the wet lab, each of which may be broken down into a sequence of sub-tasks. We conducted an in-depth analysis of the two most frequently performed sub-tasks: liquids dispensing and solids handling. Through subcoding, we identified 40 liquid dispensing typologies and 18 solid handling typologies, and evaluated the burden associated with each of these sub-tasks using the NASA TLX. These data will be of value for the design of human-centered automation tools that support, rather than displace, researchers performing manual tasks in the lab, in order to foster a safer and more accessible lab environment. 

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