Explaining decisions made with AI: A workbook (Use case 2: Machine learning for children’s social care)

Case-Based Reasoning for Explaining Probabilistic Machine Learning

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As ba...

Machine Learning Interpretability with H2O Driverless AI

Hybrid AI and Machine Learning Systems

Many different Artificial Intelligence and Machine Learning architectures and algorithms have been developed, each with their own strengths and weaknesses that make them particularly suited to certain classes of problems. Two approaches to artificial intelligence are dealt with here: subsumption architecture, and neural networks. S.A.’s are good at building up complex behaviors from sets of sim...

Imprecision in Machine Learning and AI

I this note we consider five different relevant problems in AI and machine learning. We argue that possible solutions to such problems might be achieved by replacing the probability distributions in the systems with sets of them. Such a robust approach is based on the so-called impreciseprobabilistic framework. The proposed solutions provide a persuasive justification of the imprecise framework...

Introducing Machine Learning from an Ai Perspective

1 Ingrid Russell, University of Hartford, Department of Computer Science, West Hartford, CT 06117, [email protected], 860-768-4191. 2 Zdravko Markov, Central Connecticut State University, Department of Computer Science, New Britain, CT 06050, [email protected], 860-832-2723 3 Neli Zlatareva, Central Connecticut State University, Department of Computer Science, New Britain, CT 06050, zlatarev...