A Field Experiment on Group Bias in Machine Learning Classification

Many algorithms that conceal hidden biases are being used in business and government to make important decisions in society. For example, historical data used to train machine learning (ML) algorithms are known to unintentionally discriminate against minority groups (e.g. by gender, race, etc.) depending on the nature of the observational data. Researchers typically have little to no control over the sample characteristics of the training data and often rely on technical solutions after-the-fact. Field experiments can be an effective alternative to rigorously test and potentially mitigate the effects of algorithmic bias in a number of applications. In this study, we implement a randomized controlled trial (RCT) to detect and mitigate possible bias in the context of electric vehicle (EV) usage. Expert and non-expert participants will complete a classification task using reviews from a popular electric vehicle mobile app. The data collected will be used for training a classification algorithm using neural network-based language models. In addition, policy preferences will be collected during the intervention to determine if the review classification tasks influence participant beliefs as they relate to electric vehicle infrastructure.