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

• URL: http://arxiv.org/abs/2307.06159v1
• Date: Wed, 12 Jul 2023 13:32:24 GMT
• Title: Reflective Hybrid Intelligence for Meaningful Human Control in Decision-Support Systems
• Authors: Catholijn M. Jonker, Luciano Cavalcante Siebert and Pradeep K. Murukannaiah
• Abstract summary: We introduce the notion of self-reflective AI systems for meaningful human control over AI systems. We propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches. We argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI)
• Score: 4.1454448964078585
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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