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Found 8 papers, 2 papers with code
The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering Systems
We examine how users perceive the limitations of an AI system when it encounters a task that it cannot perform perfectly and whether providing explanations alongside its answers aids users in constructing an appropriate mental model of the system's capabilities and limitations.
To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group.
Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning
Detecting mental states of human users is crucial for the development of cooperative and intelligent robots, as it enables the robot to understand the user's intentions and desires.
Intuitiveness in Active Teaching
After developing a theoretical framework of intuitiveness as a property of algorithms, we introduce an active teaching paradigm involving a prototypical two-dimensional spatial learning task as a method to judge the efficacy of human-machine interactions.
Interpretable Locally Adaptive Nearest Neighbors
When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric.
Recovering Localized Adversarial Attacks
In this contribution, we focus on the capabilities of explainers for convolutional deep neural networks in an extreme situation: a setting in which humans and networks fundamentally disagree.
Adversarial attacks hidden in plain sight
Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue.
Optimum Reject Options for Prototype-based Classification
We analyse optimum reject strategies for prototype-based classifiers and real-valued rejection measures, using the distance of a data point to the closest prototype or probabilistic counterparts.
