An Interactive Approach to Bias Identification in a Machine Teaching Task

Supervised machine learning requires labelled data examples to train models, and those examples often come from humans who may not be experts in artificial intelligence (i.e., "AI"). Currently, many resources are devoted to these labelling tasks; a majority of which are outsourced by companies to reduce costs, and oversight on such tasks can be cumbersome. Concurrently, biases in machine learning models and human cognition are a growing concern in applications of AI. In this paper, we present a machine teaching platform for non-AI experts that leverages interactive data exploration approaches to identify algorithmic and human (e.g., cognitive) biases. Our main objective is to understand how data exploration and explainability might impact the machine teacher (i.e., data labeller) and their understanding of AI, subsequently improving model performance, all while reducing potential bias concerns.