Fairness in algorithmic decision-making

Algorithmic or automated decision systems use data and statistical analyses to classify people for the purpose of assessing their eligibility for a benefit or penalty. Such systems have been traditionally used for credit decisions, and currently are widely used for employment screening, insurance eligibility, and marketing. They are also used in the public sector, including for the delivery of government services, and in criminal justice sentencing and probation decisions. Most of these automated decision systems rely on traditional statistical techniques like regression analysis. Recently, though, these systems have incorporated machine learning to improve their accuracy and fairness. These advanced statistical techniques seek to find patterns in data without requiring the analyst to specify in advance which factors to use. They will often find new, unexpected connections that might not be obvious to the analyst or follow from a common sense or theoretic understanding of the subject matter. As a result, they can help to discover new factors that improve the accuracy of eligibility predictions and the decisions based on them. In many cases, they can also improve the fairness of these decisions, for instance, by expanding the pool of qualified job applicants to improve the diversity of a company's workforce.