The Future of Fairness in AI

Lack of trust in machine learning may be the single greatest factor that prevents AI from reaching its full potential. To build this trust, data scientists must consider measures beyond predictive accuracy. Mitigating bias and ensuring fairness, along with providing robustness and transparency, are essential to ushering in a world in which machine learning systems are partners that humanity can count on.

Fairness is difficult to define for a given application domain and is not something that data scientists should be tackling alone. The inclusion of multiple stakeholders and perspectives is essential before any modeling is undertaken. After some level of consensus is reached on the desirable values and goals for an AI system, the tools provided in AIF 360 can be utilized to detect, understand, and mitigate unwanted bias. These tools address only a narrow sliver of overall fairness issues, and they should be used in conjunction with procedural and organizational measures.