An Interactive Approach to Bias Identification in a Machine Teaching Task
Tara Tressel, Claudel Rheault, Masha Krol, Chris Tyler · 2020
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.