Artificial Intelligence systems are characterized by always less interactions with humans today, leading to autonomous decision-making processes. In this context, erroneous predictions can have severe consequences. As a solution, we design and develop a set of methods derived from eXplainable AI models. The aim is to define "safety regions" in the feature space where false negatives (e.g., in a mobility scenario, prediction of no collision, but collision in reality) tend to zero. We test and compare the proposed algorithms on two different datasets (physical fatigue and vehicle platooning) and achieve quite different conclusions in terms of results that strongly depend on the level of noise in the dataset rather than on the algorithms at hand.

From Explainable to Reliable Artificial Intelligence

Narteni Sara;Cambiaso Enrico;Mongelli Maurizio
2021

Abstract

Artificial Intelligence systems are characterized by always less interactions with humans today, leading to autonomous decision-making processes. In this context, erroneous predictions can have severe consequences. As a solution, we design and develop a set of methods derived from eXplainable AI models. The aim is to define "safety regions" in the feature space where false negatives (e.g., in a mobility scenario, prediction of no collision, but collision in reality) tend to zero. We test and compare the proposed algorithms on two different datasets (physical fatigue and vehicle platooning) and achieve quite different conclusions in terms of results that strongly depend on the level of noise in the dataset rather than on the algorithms at hand.
2021
9783030840594
Logic Learning Machine
Reliable AI
Skope rules
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/462075
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