Fairness by Design: a Framework to Develop Fair AI Considering Diverse Fairness

Abstract

As artificial intelligence (AI) has been used to make social decisions such as hiring, loan decisions, and bail decisions, the issue that AI models can reflect the discriminatory bias that training data include has been pointed out. Although various majors to mitigate the bias and achieve fair AI have been taken, truly fair AI is difficult to realize because the concept of fairness is difficult to be defined unitarily because the concept of fairness can be changed according to the difference in cultures or positions. In this paper, we propose a framework called "Fairness by Design" to reflect the diverse concepts of fairness. The framework consists of a series of workshops to clarify the design requirement of AI systems with people from diverse cultures and positions, the development of interactive AI systems, aggregating the suggestions for model adjustments by people in various cultures and positions, and the mitigation of the bias considering intersectional bias. We apply this framework to the loan decision data and clarify that the concept of fairness is diverse on the basis of cultures and we can obtain the different AI models according to the cultures.