[$] Bias and ethical issues in machine-learning models

The success stories that have gathered around data analyticsdrive broader adoption of the newest artificial-intelligence-basedtechniques-but risks come along with these techniques. The large numbers of freshlyanointed data scientists piling into industry and the sensitivity of theareas given over to machine-learning models-hiring, loans, evensentencing for crime-means there is a danger of misapplied models,which is earning the attention of the public. Two sessions at the recent MinneBOS 2019 conference focused on maintaining ethics andaddressingbias in machine-learning applications.