Open, multiple, adjunct. Decision support at the time of Relational AI

In this paper, we consider some key characteristics that relational AI should exhibit to enable decision hybrid agencies that include subject-matter experts and their AI-enabled decision aids, especially when these latter ones have been developed by following a machine learning approach. We will hint at the design requirements of guaranteeing that AI tools are: open, multiple, continuous, cautious, vague, analogical and, most importantly, adjunct with respect to decision making practices. We will argue that especially adjunction is an important condition to design for. Adjunction entails the design and evaluation of human-AI interaction protocols aimed at improving AI usability, that is decision effectiveness and efficiency, while also guaranteeing user satisfaction and human and social sustainability, as well as mitigating the risk of automation bias, technology over-reliance and user deskilling. These high-level aims are compatible with the tenets of a relational approach to the design of AI tools to support decision making and collaborative practices.