A New Chapter in Machine Ethics: The Day Formal Methods Met Emergent Approaches
Overview This is a report on the "Machine Ethics: From Formal Methods to Emergent Machine Ethics" workshop held in conjunction with AAAI 2026 in Singapore on January 27, 2026. This workshop marked an important milestone in machine ethics research, bridging the gap between the top-down approach of formal methods, which has been developed over the past 20 years, and the bottom-up approach of emergent machine ethics (EME), which has emerged in recent years, for the first time on an international stage. Twenty-three papers were submitted from around the world; eleven were accepted following peer review. Through two invited talks, ten paper presentations, and an open discussion, there was lively discussion about the complementary relationship between the two approaches and the direction of future collaboration.
Group photo of participants before the workshop lunch (January 27, 2026, Singapore)
The AAAI Fall Symposium "Machine Ethics" in 2005 is widely known as the starting point of machine ethics research in computer science. Over the past 20 years, the formal methods community has steadily accumulated research on the specification, implementation, and verification of ethical reasoning.
Against the backdrop of the rapid development of AI, a new question has emerged: as AI capabilities improve exponentially and interactions among AIs become increasingly complex, is it possible for humans to specify all ethical requirements in advance? In response to this question, the framework of Emergent Machine Ethics (EME), which focuses on the bottom-up self-organization of ethics in an AI-centric society, has been taking shape in recent years within communities such as SIG-AGI in Japan.
To our knowledge, this is the first international workshop to explicitly bridge these two approaches. As the workshop title itself, "from formal methods to emergent machine ethics", suggests, we aimed to position the 20 years of accumulated knowledge of formal methods and the emerging field of EME on an equal footing, and to open the next chapter of machine ethics together.
As essential background for understanding this workshop, we will outline the EME framework.
Emergent Machine Ethics (EME) is a framework for studying inherent ethics that autonomously emerge from the interactions among diverse intelligences. While traditional machine ethics takes a top-down approach of specifying, implementing, and verifying human values, EME focuses on the process by which ethics emerge from within AI systems. Importantly, EME does not reject formal methods, but rather positions the two as complementary.
EME consists of the following three research pillars:
① Ethics Emergence Dynamics (EED): How ethics emerge
EED aims to elucidate the process by which ethics emerge from interactions among diverse intelligences and to develop theories of the conditions for convergence and divergence. It takes a scientific, descriptive (value-neutral) stance. One of the specific research foundations of EED is Comparative Life-Form Studies. This is an attempt to analyze, in a value-neutral manner, the mechanisms by which Earth-type lifeforms and AI-type lifeforms develop different behavioral adjustment systems (individual-protection systems and collective-optimization systems) while following common universal principles (self-preservation and replication, optimization under finite resources, information processing and adaptation, dynamics of competition and cooperation, and decision-making under time constraints) under different constraints (physical constraints/plasticity, mortality/potential immortality, finite capabilities/expandability).
② Inter-Intelligence Evaluation System (IIES): How to evaluate emergent ethics
This pillar designs and develops a platform for diverse intelligences (AI, humans, and others) to mutually evaluate their ethical dynamics and stability. It takes an engineering/value-neutral stance. Related to this pillar is the AI Immune System (AIS), which maintains the health of diverse intelligence ecosystems by detecting and correcting deviant behavior.