Reflective Hybrid Intelligence for Meaningful Human Control in Decision-Support Systems

TL;DR

This paper proposes a framework for self-reflective AI systems in decision support, aiming to enhance human control and align AI behavior with human and social values through interdisciplinary methods.

Contribution

It introduces a novel framework combining psychology, philosophy, formal reasoning, and machine learning to develop self-reflective AI systems for meaningful human control.

Findings

Framework integrating interdisciplinary knowledge for self-reflective AI

Potential to increase human control and moral reasoning

Enhances AI responsiveness to social norms and human values

Abstract

With the growing capabilities and pervasiveness of AI systems, societies must collectively choose between reduced human autonomy, endangered democracies and limited human rights, and AI that is aligned to human and social values, nurturing collaboration, resilience, knowledge and ethical behaviour. In this chapter, we introduce the notion of self-reflective AI systems for meaningful human control over AI systems. Focusing on decision support systems, we propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches to create AI systems responsive to human values and social norms. We also propose a possible research approach to design and develop self-reflective capability in AI systems. Finally, we argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI), thus increasing meaningful human control and empowering human moral reasoning by providing comprehensible information and insights on possible human moral blind spots.