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ItemModel Blindness: Investigating a model-based route-recommender system’s impact on decision making(Georgia Institute of Technology, 2022-12-14) Parmar, SwetaModel-Based Decision Support Systems (MDSS) are prominent in many professional domains of high consequence, such as aeronautics, emergency management, military command and control, healthcare, nuclear operations, intelligence analysis, and maritime operations. An MDSS generally uses a simplified model of the task and the operator to impose structure to the decision-making situation and provide information cues to the operator that is useful for the decision-making task. Models are simplifications, can be misspecified, and have errors. Adoption and use of these errorful models can lead to the impoverished decision-making of users. I term this impoverished state of the decision-maker model blindness. A series of two experiments were conducted to investigate the consequences of model blindness on human decision-making and performance and how those consequences can be mitigated via an explainable AI (XAI) intervention. The experiments implemented a simulated route recommender system as an MDSS with a true data-generating model (unobservable world model). In Experiment 1, the true model generating the recommended routes was misspecified to different levels to impose model blindness on users. In Experiment 2, the same route-recommender system was employed with a mitigation technique to overcome the impact of model-misspecifications on decision-making. Overall, the results of both experiments provide little support for performance degradation due to model blindness imposed by misspecified systems. The XAI intervention provided valuable insights into how participants adjusted their decision-making to account for bias in the system and deviated from choosing the model-recommended alternatives. The participants' decision strategies revealed that they could understand model limitations from feedback and explanations and could adapt their strategy to account for those misspecifications. The results provide strong support for evaluating the role of decision strategies in the model blindness confluence model. These results help establish a need for carefully evaluating model blindness during the development, implementation, and usage stages of MDSS.
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ItemPerceived Relational Risk and Perceived Situational Risk: Scale Development(Georgia Institute of Technology, 2020-11-05) Stuck, Rachel E.Interactions with technology are a significant part of daily life, both at home and at work. Understanding how to support successful human-technology interaction is essential for Engineering Psychology. Perceived relational and situational risk are key components to understanding interactions with technologies including adoption, trust, and use. However, perceived risk was only recently separated into these two distinct types: relational and situational. In addition, prior measures of perceived risk focus on hazards, not interactions with technology or automation. The goal of this dissertation was to develop and validate scales of perceived relational risk and perceived situational risk. These scales built on previous work exploring perceived risk and incorporated scale items related to affect, probability, severity, and domains. Evaluations of internal reliability, construct validity, and test-retest reliability were conducted for both scales. The items for both scales had excellent internal reliability, acceptable test-retest reliability, and support for construct validity. After determining the validity of the items, items were selected to create the final scales. These scales allow future researchers to rigorously and accurately study how perceived relational risk and perceived situational risk affect with trust, each other, and technology use.
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ItemDevelopment and validation of the situational trust scale for automated driving (STS-AD)(Georgia Institute of Technology, 2020-05-26) Holthausen, Brittany EliseTrust in automation is currently operationalized with general measures that are either self-report or behavioral in nature. However, a recent review of the literature suggests that there should be a more specific approach to trust in automation as different types of trust are influenced by different factors (Hoff & Bashir, 2015). This work is the development and validation of a measure of situational trust for the automated driving context: The Situational Trust Scale – Automated Driving (STS-AD). The first validation study showed that situational trust is a separable construct from general trust in automation and that it can capture a range of responses as seen in the difference between scores after watching a near automation failure video and non-failure videos. The second study aimed to test the STS-AD in a mid-fidelity driving simulator. Participants drove two routes: low automation (automated lane keeping only) high automation (adaptive cruise control with automated lane keeping). The results of the second study provided further support for situational trust as a distinct construct, provided insight into the factorial structure of the scale, and pointed towards a distinction between advanced driver assistance systems (ADAS) and automated driving systems (ADS). The STS-AD will revolutionize the way that trust in automation is conceptualized and operationalized. This measure opens the door to a more nuanced approach to trust in automation measurement that will inform not only how drivers interact with automated systems; but, can impact how we understand human-automation interaction as a whole.
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ItemThe role of principles in instructions for procedural tasks: timing of use, method of study, and procedural instruction specificity(Georgia Institute of Technology, 2011-11-11) Eiriksdottir, ElsaIncluding domain rules and generalities (principles) in instructions for procedural tasks is believed to help learners understand the task domain (or the system), and in turn make them better able to complete tasks. However, equivocal results of prior research indicate that principles are not always beneficial. The goal of the current research was to delineate the characteristics of the conditions under which principles are useful. In two studies I investigated the impact of the timing of principle use, the method used to study the principles, and the specificity of the procedural instructions accompanying the principles. The first study showed that the timing of principle use (studying the principles before, during, or after completing training tasks) did not affect declarative (knowledge of the system) or procedural learning (troubleshooting task performance). Therefore, the commonly advocated idea that principles should be provided before task engagement was not supported. Neither was the hypothesis that using principles while solving tasks would enhance procedural learning. When learners summarized the principles, they demonstrated better declarative learning compared to when they just read the principles. Better declarative learning was associated with better procedural learning, but the relationship between understanding and using a system is likely not as direct as often assumed. In the second study declarative and procedural learning were enhanced when the principles were accompanied by general rather than detailed procedural instructions. General procedural instructions appeared to encourage task engagement and the effective use of principles although this effect was reduced if leaners were required to summarize the principles rather than simply read them. Together the results of the two studies reveal how the learning situation and instructional materials can be constructed to create conditions where principles enhance learning and subsequent performance.
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ItemRepresentations for learning dance in a computational dance tutor(Georgia Institute of Technology, 1999-08) Sukel, Katherine E.