4 papers · 1 filter
Explorations of the Softmax Space: Knowing When the Neural Network Doesn't Know
Daniel Sikar, Artur d'Avila Garcez, Tillman Weyde
Ensuring the reliability of automated decision-making based on neural networks will be crucial as Artificial Intelligence systems are deployed more widely in critical situations. T…
When to Accept Automated Predictions and When to Defer to Human Judgment?
Daniel Sikar, Artur Garcez, Tillman Weyde +2
Ensuring the reliability and safety of automated decision-making is crucial. It is well-known that data distribution shifts in machine learning can produce unreliable outcomes. Thi…
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
Daniel Sikar, Artur Garcez, Robin Bloomfield +6
This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM…
Evaluation of autonomous systems under data distribution shifts
Daniel Sikar, Artur Garcez
We posit that data can only be safe to use up to a certain threshold of the data distribution shift, after which control must be relinquished by the autonomous system and operation…