Developing Confidence in Machine Learning Results
Authors: Jessica Baweja, Brett Jefferson, Corey Fallon
Abstract: As the field of deep learning has emerged in recent years, the amount of knowledge and expertise that data scientists are expected to absorb and maintain has correspondingly increased. One of the challenges experienced by data scientists working with deep learning models is developing confidence in the accuracy of their approach and the resulting findings. In this study, we conducted semi-structured interviews with data scientists at a national laboratory to understand the processes that data scientists use when attempting to develop their models and the ways that they gain confidence that the results they obtained were accurate. These interviews were analysed to provide an overview of the techniques currently used when working with machine learning (ML) models. Opportunities for collaboration with human factors researchers to develop new tools are identified.
Keywords: human factors, machine learning, data science
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