End-user-centered Interactive Explanatory Relational Learning with Inductive Logic Programming

This paper shows how improved interactive interfaces can afford end-users better control over expressive logic-based machine learners in order to help circumvent the problem of overfitting on confounding factors in complex relational data. Prior work has shown such confounders commonly occur in real-world data sets in the form of incidental correlations arising from sampling biases, modelling artifacts, labelling errors or simply due to chance occurrences in the training data. Inductive Logic Programming (ILP) is a logical machine learning methodology that can help users address this problem by providing them with hypotheses that are readily understandable and editable by humans. Moreover, because ILP operates directly on relational data which need not be collapsed into finite feature vectors, ILP potentially enables identification of complex relational confounders - which have not been studied until now. This paper proposes an interactive dashboard to make a state-of-the-art interactive ILP system accessible to end-users without a background in computational logic. We present a proof-of-principal case study which shows how users can intuitively identify and circumvent relational confounders in a new synthetic dataset that we derived from prior work in this field.