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1. Emotion AI Use in U.S. Mental Healthcare: Potentially Unjust and Techno-Solutionist

   https://doi.org/10.1145/3637324  

   Roemmich, Kat; Corvite, Shanley; Pyle, Cassidy; Karizat, Nadia; Andalibi, Nazanin (April 2024, Proceedings of the ACM on Human-Computer Interaction)

   Emotion AI, or AI that claims to infer emotional states from various data sources, is increasingly deployed in myriad contexts, including mental healthcare. While emotion AI is celebrated for its potential to improve care and diagnosis, we know little about the perceptions of data subjects most directly impacted by its integration into mental healthcare. In this paper, we qualitatively analyzed U.S. adults' open-ended survey responses (n = 395) to examine their perceptions of emotion AI use in mental healthcare and its potential impacts on them as data subjects. We identify various perceived impacts of emotion AI use in mental healthcare concerning 1) mental healthcare provisions; 2) data subjects' voices; 3) monitoring data subjects for potential harm; and 4) involved parties' understandings and uses of mental health inferences. Participants' remarks highlight ways emotion AI could address existing challenges data subjects may face by 1) improving mental healthcare assessments, diagnoses, and treatments; 2) facilitating data subjects' mental health information disclosures; 3) identifying potential data subject self-harm or harm posed to others; and 4) increasing involved parties' understanding of mental health. However, participants also described their perceptions of potential negative impacts of emotion AI use on data subjects such as 1) increasing inaccurate and biased assessments, diagnoses, and treatments; 2) reducing or removing data subjects' voices and interactions with providers in mental healthcare processes; 3) inaccurately identifying potential data subject self-harm or harm posed to others with negative implications for wellbeing; and 4) involved parties misusing emotion AI inferences with consequences to (quality) mental healthcare access and data subjects' privacy. We discuss how our findings suggest that emotion AI use in mental healthcare is an insufficient techno-solution that may exacerbate various mental healthcare challenges with implications for potential distributive, procedural, and interactional injustices and potentially disparate impacts on marginalized groups. 

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2. New Frontiers of Information Extraction

   https://doi.org/10.18653/v1/2022.naacl-tutorials.3  

   Chen, Muhao; Huang, Lifu; Li, Manling; Zhou, Ben; Ji, Heng; Roth, Dan (January 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials)

   This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. Particularly, this tutorial will provide audience with a systematic introduction to recent advances of IE, by answering several important research questions. These questions include (i) how to develop an robust IE system from noisy, insufficient training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system’s cross-lingual, cross-domain, cross-task and cross-modal transferability? (iii) how to precisely support extracting structural information with extremely fine-grained, diverse and boundless labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as NLI, QA or summarization, and pre-trained language models? (v) how to acquire knowledge to guide the inference of IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation. 

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3. Effects of Environmental Noise Levels on Patient Handoff Communication in a Mixed Reality Simulation

   https://doi.org/10.1145/3562939.3565627  

   Gottsacker, Matt; Norouzi, Nahal; Schubert, Ryan; Guido-Sanz, Frank; Bruder, Gerd; Welch, Gregory (November 2022, 28th ACM Symposium on Virtual Reality Software and Technology)

   When medical caregivers transfer patients to another person’s care (a patient handoff), it is essential they effectively communicate the patient’s condition to ensure the best possible health outcomes. Emergency situations caused by mass casualty events (e.g., natural disasters) introduce additional difficulties to handoff procedures such as environmental noise. We created a projected mixed reality simulation of a handoff scenario involving a medical evacuation by air and tested how low, medium, and high levels of helicopter noise affected participants’ handoff experience, handoff performance, and behaviors. Through a human-subjects experimental design study (N = 21), we found that the addition of noise increased participants’ subjective stress and task load, decreased their self-assessed and actual performance, and caused participants to speak louder. Participants also stood closer to the virtual human sending the handoff information when listening to the handoff than they stood to the receiver when relaying the handoff information. We discuss implications for the design of handoff training simulations and avenues for future handoff communication research. 

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4. Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior

   https://doi.org/10.1115/1.4048580  

   Pillai, Priya P.; Burnell, Edward; Wang, Xiqing; Yang, Maria C. (December 2020, Journal of Mechanical Design)

   null (Ed.)

   Abstract Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set-based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed the present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices. 

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5. Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration

   https://doi.org/10.1115/DETC2020-22626  

   Pillai, P.; Burnell, E.; Wang, X.; Yang, M. C. (August 2020, Proceedings of the ASME Design Engineering Technical Conferences)

   null (Ed.)

   Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worstcase and multiplying uncertain parameters with safety factors, or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing an inexpressive account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in experimental participant’s understandings of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices. 

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