Browsing by Subject "Trustworthiness"
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Item Exploring the nomological net of trust in leadership: an empirical examination of antecedents, moderators, and outcomes(2012-12) Rasch, Rena LenoreTo fully understand human interactions in the workplace, we must understand the role trust plays. My dissertation is a general investigation of trust between subordinates and leaders within an organizational context. Using a diverse sample of US employees, I examined the relative importance of three key trust determinants: leader benevolence, competence, and integrity. I also examined the role trait trust plays in the trust nomological net. I examined previously posited, yet untested, moderators of the trustworthiness-trust relationship. Lastly, I tested the contextual effects of risk and formal controls on the relationship between employees' trust in leadership and their turnover intentions. I found an individual's propensity to trust seems to affect trust in leadership through perceptions of leader trustworthiness. Leaders can inspire trust by being capable, kind, and honest. Leader integrity is the most important direct determinant of trust in leadership. Despite theoretical arguments, relationship length and job complexity have no bearing on the importance of the direct determinants of trust in leadership. A manager may use trust to influence his/her staff, who are more willing to assume risk on their manager's behalf. Trust may act as a substitute for costly and rigid formal control mechanisms, like legal contracts. Despite theoretical arguments, situational risk in the form of organizational change, whether perceived or actual, does not magnify the importance of trust in leadership to turnover intentions. Still, trust in leadership is important to predicting turnover intentions, even beyond job satisfaction and organizational commitment.Item Towards Human-Like Machine Intelligence: Generalizability, Transferability, and Trustworthiness(2022-06) Luo, YanAcquiring human-like machine intelligence is a long-standing goal of machine learning. Thanks to the availability of large-scale datasets and the GPU acceleration, modern learning methods achieve remarkable success. Although it surpasses humans on several tasks, e.g., the game of go, there is still a gap between machine intelligence and human intelligence. The reasons are two-fold. Firstly, how the human brain produces intelligence is still little-known, and how to apply the mechanisms that are discovered in the research of neuroscience to machine intelligence remains unclear. Secondly, human intelligence has been proven to be versatile to a wide variety of capacities, e.g., abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, problem-solving, etc. It is unclear how to comprehensively measure human intelligence. There is no evidence thus far that machine intelligence can be a replacement for human intelligence in a wide range of real-world applications. Instead of diving into human brain neurons, Piaget studies human intelligence from the aspect of cognitive development along the key periods of growth. In Piaget's theory, two processes are closely related to human intelligence, that is, assimilation and accommodation. Assimilation aims to fit new information into existing cognitive schemas, while accommodation aims to take new information in one's environment and alter existing schemas to fit in the new information. We focus on three specific characteristics, i.e., generalizability, transferability, and trustworthiness, that center around assimilation and accommodation. Specifically, generalizability is an important yet generic concept in machine learning. Instead, we study the generalizability that takes place in the process of fitting new information associated to unknown classes into the knowledge w.r.t. known classes. Secondly, we explore how to transfer the knowledge learned from the source domain samples to the target domain with very few target-domain examples. Last but not least, there is still a gap between state-of-the-art learning-based approaches and a perfect one. Therefore, there is a critical need to understand the trustworthiness of machine intelligence.