CAREER: Fair Artificial Intelligence for Intelligent Humans: Removing the Barriers to Deployment of Fair AI Technologies

There is growing awareness that artificial intelligence (AI) and machine learning systems can in some cases behave in unfair and discriminatory ways with harmful consequences in many areas including criminal justice, hiring, medicine, and college admissions. Techniques for ensuring AI fairness have received a lot of attention in the AI literature. However, these techniques are yet to see a substantial degree of deployment in real systems, which has thus-far limited their real-world impact.

This is likely due in part to several practical challenges for deploying fair AI technologies. Firstly, the conventional wisdom is that fairness brings a cost in prediction performance which could affect an organization's bottom-line. Secondly, it is difficult to know which mathematical definition of AI fairness is appropriate to adopt since the definitions conflict with each other and encode different value systems.

Finally, there is a chicken-and-egg problem, in that public pressure for an organization to adopt fairness considerations into an AI system only increases after this has been successfully demonstrated elsewhere. This research will develop technical solutions to resolve these human-facing barriers for the adoption of AI fairness techniques, thereby increasing deployment and the subsequent positive real-world impact.

To resolve the practical limitations of fair AI techniques, this research incorporates human-centered considerations into the design and execution of fair AI algorithms, connecting and advancing the state of the art in statistical machine learning, fair AI, and human-centered AI. The first track of the project will develop methods for obtaining “fairness for free,” in which the fairest possible solution is found when sacrificing little-to-no performance.

The researchers will design black-box, gray-box, and white-box approaches to this task. Then, the second track of the research will focus on developing explainable AI and data visualization techniques to help humans assess and trade off the consequences of different competing notions of fairness. A key step to accomplish this is to create a unifying fairness framework which systematically encodes the space of possible fairness metrics.

Finally, in the third track of the project, the researchers will develop practical solutions to several real-world applications of AI fairness, including the allocation of medical resources, and AI-based career counseling. The solutions will involve both applied and fundamental AI research, and will facilitate the evaluation of the methods developed in the first two tracks.

The project also includes initiatives for outreach, broadening participation in science, technology, engineering, and mathematics (STEM) fields, training and educating graduate students, and curriculum development.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.