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Found 6 papers, 0 papers with code
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Our results show that the selective relevance method can not only provide insight on the role played by motion in the model's decision -- in effect, revealing and quantifying the model's spatial bias -- but the method also simplifies the resulting explanations for human consumption.
Sanity Checks for Saliency Metrics
Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i. e. their "fidelity").
Illuminated Decision Trees with Lucid
The Lucid methods described by Olah et al. (2018) provide a way to inspect the inner workings of neural networks trained on image classification tasks using feature visualization.
Stakeholders in Explainable AI
There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable.
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable.
BIG-bench Machine Learning
Interpretable Machine Learning
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