Recent work has raised concerns on the risk of unintended bias in AI systems
being used nowadays that can affect individuals unfairly based on race, gender
or religion, among other possible characteristics. While a lot of bias metrics
and fairness definitions have been proposed in recent years, there is no
consensus on which metric/definition should be used and there are very few
available resources to operationalize them. Therefore, despite recent
awareness, auditing for bias and fairness when developing and deploying AI
systems is not yet a standard practice. We present Aequitas, an open source
bias and fairness audit toolkit that is an intuitive and easy to use addition
to the machine learning workflow, enabling users to seamlessly test models for
several bias and fairness metrics in relation to multiple population
sub-groups. Aequitas facilitates informed and equitable decisions around
developing and deploying algorithmic decision making systems for both data
scientists, machine learning researchers and policymakers.

研究人员开发了一个名为 Aequitas 的开源工具包，该工具包可帮助数据科学家、机器学习研究人员和政策制定者测试多个偏差和公平度量标准，以实现对算法决策制定系统进行有根据的和公平的决策。