Google AI today released TensorFlow Constrained Optimization (TFCO), a supervised machine learning library built for training machine learning models on multiple metrics and "optimizing inequality-constrained problems." The library is designed to help address issues like fairness constraints and predictive parity and help machine learning practitioners better understand things like true positive rates on residents of certain countries, or recall illness diagnoses depending on age and gender. In tests with a Wikipedia data set, the library achieved lower false-positive rates when predicting whether a comment on a Wiki is toxic based on race, religion, gender identity, or sexuality, while maintaining similar accuracy rates. TFCO is made to "take into account the societal and cultural factors necessary to satisfy real-world requirements," said Andrew Zaldivar on behalf of the TFCO team today in a Google AI blog post. "The ability to express many fairness goals as rate constraints can help drive progress in the responsible development of machine learning, but it also requires developers to carefully consider the problem they are trying to address," he said.