[$] Bias and ethical issues in machine-learning models
The success stories that have gathered around data analytics
drive broader adoption of the newest artificial-intelligence-based
techniques—but risks
come along with these techniques. The large numbers of freshly
anointed data scientists piling into industry and the sensitivity of the
areas given over to machine-learning models—hiring, loans, even
sentencing 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 and
addressing
bias in machine-learning applications.