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Found 10 papers, 3 papers with code
Taxonomy of Machine Learning Safety: A Survey and Primer
The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations.
Shifting Transformation Learning for Out-of-Distribution Detection
In this paper, we propose a simple framework that leverages a shifting transformation learning setting for learning multiple shifted representations of the training set for improved OOD detection.
Ranked #9 on
Anomaly Detection
on Unlabeled CIFAR-10 vs CIFAR-100
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task in the post-truth era.
Practical Solutions for Machine Learning Safety in Autonomous Vehicles
Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning.
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations.
Open Issues in Combating Fake News: Interpretability as an Opportunity
Combating fake news needs a variety of defense methods.
Combating Fake News with Interpretable News Feed Algorithms
Nowadays, artificial intelligence algorithms are used for targeted and personalized content distribution in the large scale as part of the intense competition for attention in the digital media environment.
Social and Information Networks Computers and Society
A Survey of Evaluation Methods and Measures for Interpretable Machine Learning
The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life.
Human-Computer Interaction
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
We demonstrate our benchmark's utility for quantitative evaluation of model explanations by comparing it with human subjective ratings and ground-truth single-layer segmentation masks evaluations.
