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Full-Text Articles in Social and Behavioral Sciences

Allograft Anterior Cruciate Ligament Reconstruction Fails At A Greater Rate In Patients Younger Than 34 Years, Camryn B. Petit, Jed A. Diekfuss, Shayla M. Warren, Kim D. Barber Foss, Melanie Valencia, Staci M. Thomas, Erich Petushek, Spero G. Karas, Kyle E. Hammond, Mathew W. Pombo, Sameh A. Labib, Timothy S. Maughon, Bryan J. Whitfield, Gregory D. Myer, John W. Xerogeanes, Joseph D. Lamplot Aug 2023

Allograft Anterior Cruciate Ligament Reconstruction Fails At A Greater Rate In Patients Younger Than 34 Years, Camryn B. Petit, Jed A. Diekfuss, Shayla M. Warren, Kim D. Barber Foss, Melanie Valencia, Staci M. Thomas, Erich Petushek, Spero G. Karas, Kyle E. Hammond, Mathew W. Pombo, Sameh A. Labib, Timothy S. Maughon, Bryan J. Whitfield, Gregory D. Myer, John W. Xerogeanes, Joseph D. Lamplot

Michigan Tech Publications

Purpose: The purposes of this study were to characterize the secondary anterior cruciate ligament (ACL) injury rates after primary allograft anterior cruciate ligament reconstruction (ACLR) and to identify the age cut-score at which the risk of allograft failure decreases. Methods: All patients who underwent primary ACLR within a single orthopaedic department between January 2005 and April 2020 were contacted at a minimum of 2 years post-ACLR to complete a survey regarding complications experienced post-surgery, activity level, and perceptions of knee health. Patients were excluded for incidence of previous ACLR (ipsilateral or contralateral) and/or age younger than 14 years. Relative proportions …


Increasing The Value Of Xai For Users: A Psychological Perspective, Robert R. Hoffman, Timothy Miller, Gary Klein, Shane T. Mueller, William J. Clancey Jul 2023

Increasing The Value Of Xai For Users: A Psychological Perspective, Robert R. Hoffman, Timothy Miller, Gary Klein, Shane T. Mueller, William J. Clancey

Michigan Tech Publications

This paper summarizes the psychological insights and related design challenges that have emerged in the field of Explainable AI (XAI). This summary is organized as a set of principles, some of which have recently been instantiated in XAI research. The primary aspects of implementation to which the principles refer are the design and evaluation stages of XAI system development, that is, principles concerning the design of explanations and the design of experiments for evaluating the performance of XAI systems. The principles can serve as guidance, to ensure that AI systems are human-centered and effectively assist people in solving difficult problems.


Transoptic Landscape Analysis: Multidimensional Landscapes Of A Multinational Wales, Mark Rhodes May 2023

Transoptic Landscape Analysis: Multidimensional Landscapes Of A Multinational Wales, Mark Rhodes

Michigan Tech Publications

In this article I propose a novel extension to landscape analysis through multidimensional understandings, including—yet reaching beyond—tangible and into more-than-representational understandings of landscape. This "transoptic" approach to landscape, breaking away from strictly searching for visual representations of culture, allows for sonic, experiential, and emotional layers of meaning embedded in landscapes to emerge from their plural cultural and historical contexts. Memory, and the production and experience of that memory in the landscape, benefit from this transoptic understanding. Utilizing memory work, which includes both memory production and consumption, in Wales as a case study, I employ a transoptic landscape analysis to approach …


The Plausibility Transition Model For Sensemaking, Gary Klein, Mohammadreza Jalaeian, Robert R. Hoffman, Shane Mueller May 2023

The Plausibility Transition Model For Sensemaking, Gary Klein, Mohammadreza Jalaeian, Robert R. Hoffman, Shane Mueller

Michigan Tech Publications

When people make plausibility judgments about an assertion, an event, or a piece of evidence, they are gauging whether it makes sense that the event could transpire as it did. Therefore, we can treat plausibility judgments as a part of sensemaking. In this paper, we review the research literature, presenting the different ways that plausibility has been defined and measured. Then we describe the naturalistic research that allowed us to model how plausibility judgments are engaged during the sensemaking process. The model is based on an analysis of 23 cases in which people tried to make sense of complex situations. …


The Plausibility Transition Model For Sensemaking, Gary Klein, Mohammadreza Jalaeian, Robert R. Hoffman, Shane T. Mueller May 2023

The Plausibility Transition Model For Sensemaking, Gary Klein, Mohammadreza Jalaeian, Robert R. Hoffman, Shane T. Mueller

Michigan Tech Publications

When people make plausibility judgments about an assertion, an event, or a piece of evidence, they are gauging whether it makes sense that the event could transpire as it did. Therefore, we can treat plausibility judgments as a part of sensemaking. In this paper, we review the research literature, presenting the different ways that plausibility has been defined and measured. Then we describe the naturalistic research that allowed us to model how plausibility judgments are engaged during the sensemaking process. The model is based on an analysis of 23 cases in which people tried to make sense of complex situations. …


Evaluating Machine-Generated Explanations: A “Scorecard” Method For Xai Measurement Science, Robert R. Hoffman, Mohammadreza Jalaeian, Connor Tate, Gary Klein, Shane T. Mueller May 2023

Evaluating Machine-Generated Explanations: A “Scorecard” Method For Xai Measurement Science, Robert R. Hoffman, Mohammadreza Jalaeian, Connor Tate, Gary Klein, Shane T. Mueller

Michigan Tech Publications

Introduction: Many Explainable AI (XAI) systems provide explanations that are just clues or hints about the computational models-Such things as feature lists, decision trees, or saliency images. However, a user might want answers to deeper questions such as How does it work?, Why did it do that instead of something else? What things can it get wrong? How might XAI system developers evaluate existing XAI systems with regard to the depth of support they provide for the user's sensemaking? How might XAI system developers shape new XAI systems so as to support the user's sensemaking? What might be a useful …


Executive Functions And Psychopathology Dimensions In Deficit And Non-Deficit Schizophrenia, Maksymilian Bielecki, Ernest Tyburski, Piotr Plichta, Monika Mak, Jolanta Kucharska-Mazur, Piotr Podwalski, Katarzyna Rek-Owodziń, Katarzyna Waszczuk, Leszek Sagan, Shane Mueller, Anna Michalczyk, Błażej Misiak, Jerzy Samochowiec Mar 2023

Executive Functions And Psychopathology Dimensions In Deficit And Non-Deficit Schizophrenia, Maksymilian Bielecki, Ernest Tyburski, Piotr Plichta, Monika Mak, Jolanta Kucharska-Mazur, Piotr Podwalski, Katarzyna Rek-Owodziń, Katarzyna Waszczuk, Leszek Sagan, Shane Mueller, Anna Michalczyk, Błażej Misiak, Jerzy Samochowiec

Michigan Tech Publications

This study: (a) compared executive functions between deficit (DS) and non-deficit schizophrenia (NDS) patients and healthy controls (HC), controlling premorbid IQ and level of education; (b) compared executive functions in DS and NDS patients, controlling premorbid IQ and psychopathological symptoms; and (c) estimated relationships between clinical factors, psychopathological symptoms, and executive functions using structural equation modelling. Participants were 29 DS patients, 44 NDS patients, and 39 HC. Executive functions were measured with the Mazes Subtest, Spatial Span Subtest, Letter Number Span Test, Color Trail Test, and Berg Card Sorting Test. Psychopathological symptoms were evaluated with the Positive and Negative Syndrome …


Measures For Explainable Ai: Explanation Goodness, User Satisfaction, Mental Models, Curiosity, Trust, And Human-Ai Performance, Robert R. Hoffman, Shane Mueller, Gary Klein, Jordan Litman Feb 2023

Measures For Explainable Ai: Explanation Goodness, User Satisfaction, Mental Models, Curiosity, Trust, And Human-Ai Performance, Robert R. Hoffman, Shane Mueller, Gary Klein, Jordan Litman

Michigan Tech Publications

If a user is presented an AI system that portends to explain how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? This question entails some key concepts of measurement such as explanation goodness and trust. We present methods for enabling developers and researchers to: (1) Assess the a priori goodness of explanations, (2) Assess users' satisfaction with explanations, (3) Reveal user's mental model of an AI system, (4) Assess user's curiosity or need for explanations, (5) Assess whether the user's trust and reliance on the AI are …