Organizational Behavior and Human Decision Processes

By Jennifer M. Logga, Julia A. Minsona, Don A. Mooreb. 2019

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Citations in the corpus

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “65 The possible inﬂuences of automation bias or algorithm aversion on AI used for decision support are unclear.”
Algorithmic management in a work context (2021, background): “Balancing aversion to algorithms is, however, a trend for cognitive complacency (Logg et al., 2019).”
Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “In such settings, team members are less qualified to evaluate whether an AI model produces reasonable results 117 while simultaneously being more prone to trust AI 116 .”
A systematic review of algorithm aversion in augmented decision making (2019, background): “With recent work suggesting that humans display algorithm appreciation (Logg et al., 2019) rather than algorithm aversion, the need for clarity seems especially pressing.”
Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments (2019, background): “Judges may also be more reluctant to rely on risk assessments, believing that their own judgment is superior: previous research has shown that people with more expertise are less willing to take advice [38, 48, 50], and a recent survey found that less than 10%”
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence (2021, background): “Given that decision makers treat system advice very differently from human advice (Dietvorst et al. 2015, Logg et al. 2019, Longoni et al. 2019), it is still unclear which types of metacognitions allow decision makers to successfully include system advice in their decision making process.”
In AI we trust? Perceptions about automated decision-making by artificial intelligence (2020, unknown): “The same results, however, were not seen in other contexts (Logg 2017).”
Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases (2019, unknown): “Prior research also discusses how AI and robots will replace the labor and work force (Brynjolfsson and Mitchell 2017, Lu et al. 2018).”
The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust (2022, methodology): “This hypothesis was generated via the theoretical notion and empirical evidence for algorithm appreciation (Logg et al., 2019).”
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies (2021, background): “Efficacy Subjective Self-rated error/accuracy [37, 81, 137], perceived performance improvement [32], confidence in the decisions [54, 55, 60, 95], soundness of participants’ mental models [76] Objective Accuracy/error [11, 13, 16, 20, 20, 24, 26, 42, 43, 52, 58, 62, 68, 73, 74, 79–81, 90, 92, 94, 97”
Task-Dependent Algorithm Aversion (2019, methodology): “Finally, Yeomans et al. (2019) found that participants relied less on an algorithm than on humans for the task of predicting joke funniness, while Logg, Minson, and Moore (2019) found that participants relied more on algorithms than on humans for numerical tasks with an objectively correct answer.”
Initial validation of the general attitudes towards Artificial Intelligence Scale (2020, unknown): “Preferences for AI over humans has also been observed in different contexts, related to expertise, in a phenomenon named “ algo-rithm appreciation ” (Logg, Minson, & Moore, 2019).”