Explaining Machine Learning Decisions

Explaining machine learning models in sales predictions

A complexity of business dynamics often forces decision-makers to make decisions based on subjective mental models, reflecting their experience. However, research has shown that companies perform better when they apply data-driven decision-making. This creates an incentive to introduce intelligent, data-based decision models, which are comprehensive and support the interactive evaluation of dec...

CURRENT PROJECTS Explaining Machine Learning Models

Explaining Complex Scheduling Decisions

The work presented in this paper describes the explanation facility of an intelligent scheduling software framework that has been customized and deployed in a variety of domains. The customizability of the framework allows the software to develop a valid schedule that reflects each domain’s specific preferences and constraints. In all domains, the software quickly solves a complex scheduling pr...

Explaining Best Decisions via Argumentation

This paper presents an argumentation-based multi-attribute decision making model, where decisions made can be explained in natural language. More specifically, an explanation for a decision is obtained from a mapping between the given decision framework and an argumentation framework, such that best decisions correspond to admissible sets of arguments, and the explanation is generated automatic...

Auto Classifier Explaining Customers a Machine-Learning Model

When explaining customers that the artificial intelligence approach of our products automatically adapts document classifiers on training documents by applying statistical machine-learning, their reaction is similar like if we would tell them about an artificial intelligence in car breaks. Most likely they would dislike it, because they want full control on their data processors. Hence, we sell...