Human-Centric AI to Mitigate AI Biases

Abstract

The global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This article aims to tackle part of the biases in artificial intelligence by implementing a human-centric AI to help decision-makers in organizations. It relies on the results of two design science research (DSR) projects: SCHOPPER and VRAILEXIA. These two design projects operationalize the human-centric AI approach with two complementary stages: 1) the first installs a human-in-loop informed design process, and 2) the second implements a usage architecture that aggregates AI and humans. The proposed framework offers many advantages such as permitting to integrate of human knowledge into the design and training of the AI, providing humans with an understandable explanation of their predictions, and driving the advent of augmented intelligence that can turn algorithms into a powerful counterweight to human decision-making errors and humans as a counterweight to AI biases.