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Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision
In this paper we studied this problem for object detection and recognition. We conducted several data annotation experiments to measure inter annotator agreement and consistency, as well as how the selection of ground truth impacts the perceived algorithm performance. We propose a methodology to monitor the quality of annotations during the labeling of images and how it can be used to measure performance.
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems.
Improving Community Resiliency and Emergency Response With Artificial Intelligence
New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases.
Recommending Training Set Sizes for Classification
Based on a comprehensive study of 20 established data sets, we recommend training set sizes for any classification data set.
