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Found 5 papers, 5 papers with code
Scope Compliance Uncertainty Estimate
The zeitgeist of the digital era has been dominated by an expanding integration of Artificial Intelligence~(AI) in a plethora of applications across various domains.
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication.
Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring
Limitations in setting SafeML up properly include the lack of a systematic approach for determining, for a given application, how many operational samples are needed to yield reliable distance information as well as to determine an appropriate distance threshold.
StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures
This work focuses on the use of SafeML for time series data, and on reliability and robustness estimation of ML-forecasting methods using statistical distance measures.
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure
Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems.
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