Exploring the Explicit Modelling of Bias in Machine Learning Classifiers: A Deep Multi-label ConvNet Approach *

This paper addresses the problem that many machine learning classifiers make decisions based on data that are biased and can therefore result in prejudiced decisions. For example, in education (which this paper focuses on) a student may be rejected from a course based on historical decisions in the data that only exist due to historical biases in society or due to the skewed sampling of the data. Other approaches to dealing with bias in data include resampling methods (to counter imbalanced samples) and dimensionality reduction (to focus only on relevant features to the classification task). In this paper, we explore issues of modelling bias explicitly so that we can identify the types of bias and whether they are accounting for inflated predictive accuracies. In particular, we compare graphical model approaches to building classifiers, that are transparent in how they make decisions, with two forms of Deep Multi-label Convolutional Neural Networks to investigate if models can be built that maximise accuracy and minimise bias. We carry out this comparison on student entry and performance data from a higher educational institution.