Epistemic Injustice as a Philosophical Conception for Considering Fairness and Diversity in Human-centered AI Principles

Abstract

The sheer quantity of information in the modern world has increased significantly, which exceeds the volume that can be managed using human power. Although information is necessary for decision making, excessive information is not beneficial for proper decision making. Therefore, data mining conducted using machine learning and artificial intelligence (AI)-assisted decision-making systems are increasingly being used in our society. However, problems, such as discriminatory decisions and the promulgation of injustice by AI, have been exposed recently. In response to this, numerous countries and organizations have recently announced a set of AI principles based on the concept of human-centered AI that fosters human values. The principles call for understanding diversity, ensuring fairness, and eliminating discrimination in the use of AI. To implement these values in AI systems, having a philosophical understanding of the structure of injustice in human knowledge production is essential. The problems of injustice and discrimination in knowledge production have recently been categorized as ``epistemic injustice'' in philosophy and epistemology, and the theories explaining these phenomena are becoming more sophisticated. This paper aims to contribute to the understanding of ``human-centered'' AI by connecting the philosophical concept of ``epistemic injustice'' to the discussion of AI ethical principles. It further points out that the issue of injustice and unfairness in AI use is not only a social–ethical as well as an epistemic concern.