Human Information Interaction, Artificial Intelligence, and Errors

Russell, Stephen; Moskowitz, Ira S.; Raglin, Adrienne

doi:10.1007/978-3-319-59719-5_4

Cited by 15 publications

(9 citation statements)

References 72 publications

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“…Understanding how the AI works, what it is capable of, and maintaining a proper amount of trust is crucial for optimal system performance. AI systems are likely to make errors (Prahl & Goh, 2021; Russell et al, 2017). It is common to have a human providing oversight (Jarrahi, 2018).…”

Section: Artificial Intelligencementioning

confidence: 99%

Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign

Garcia

Mishler

Xiao

et al. 2022

Journal of Cognitive Engineering and Decision Making

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Automated Driving Systems (ADS), like many other systems people use today, depend on successful Artificial Intelligence (AI) for safe roadway operations. In ADS, an essential function completed by AI is the computer vision techniques for detecting roadway signs by vehicles. The AI, though, is not always reliable and sometimes requires the human’s intelligence to complete a task. For the human to collaborate with the AI, it is critical to understand the human’s perception of AI. In the present study, we investigated how human drivers perceive the AI’s capabilities in a driving context where a stop sign is compromised and how knowledge, experience, and trust related to AI play a role. We found that participants with more knowledge of AI tended to trust AI more, and those who reported more experience with AI had a greater understanding of AI. Participants correctly deduced that a maliciously manipulated stop sign would be more difficult for AI to identify. Nevertheless, participants still overestimated the AI’s ability to recognize the malicious stop sign. Our findings suggest that the public do not yet have a sufficiently accurate understanding of specific AI systems, which leads them to over-trust the AI in certain conditions.

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Section: Artificial Intelligencementioning

confidence: 99%

Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign

Garcia

Mishler

Xiao

et al. 2022

Journal of Cognitive Engineering and Decision Making

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“…Nowadays, people and computers can interact with each other in different ways, which is a process of information exchange between people and computers to complete certain tasks. Information interaction between people is also carried out in a certain way (Russell et al, 2017). Forbrig et al (2017) introduced the classification of human-computer interaction.…”

Section: Related Workmentioning

confidence: 99%

Adaptive information sharing approach for crowd networks based on two stage optimization

Wang

Pan

et al. 2019

IJCS

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Purpose -This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.Design/methodology/approach -This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.Findings -The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information

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“…More specifically, the quality of the machine‐learned output is completely unknown until such a (contextual) time as an observation is obtained that indicates there is a potential issue, which usually occurs at runtime and can cause catastrophic system problems. This type of problem is frequently the case with contemporary ML algorithms and subsequent AI, leading to the rise of adversarial exploitation or disastrous errors or in the best case, greater ambiguity in risk and decision‐making (Russell and Moskowitz 2016).…”

Section: Figurementioning

confidence: 99%

Uncertain Context: Uncertainty Quantification in Machine Learning

2019

Self Cite

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Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning-related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.

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Human Information Interaction, Artificial Intelligence, and Errors

Cited by 15 publications

References 72 publications

Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign

Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign

Adaptive information sharing approach for crowd networks based on two stage optimization

Uncertain Context: Uncertainty Quantification in Machine Learning

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