public:t-709-aies-2025:aies-2025:trust_explanation_meaning
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T-713-MERS-2025 Main
Link to Lecture Notes
TRUST & EXPLANATIONS
Engineered Predictability
What It Is | The ability of an outsider to predict the behavior of a controller based on some information. |
Why It Is Important | Predicting the behavior of (semi-) autonomous machines is important if we want to ensure their safe operation, or be sure that they do what we want them to do. |
How To Do It | Predicting the future behavior of ANNs (of any kind) is easier if we switch off their learning after they have been trained, because there exists no method for predicting where their development will lead them if they continue to learn after the leave the lab. Predicting ANN behavior on novel input can be done statistically, but there is no way to be sure that novel input will not completely reverse their behavior. There are very few if any methods for giving ANNs the ability to judge the “novelty” of any input, which might to some extent possibly help with this issue. Reinforcement learning addresses this by only scaling to a handful of variables with known max and min. |
Correlation vs. Causation |
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