Philosophical Specification of Empathetic Ethical Artificial Intelligence
Michael Timothy Bennett, Yoshihiro Maruyama

TL;DR
This paper proposes a novel philosophical framework for ethical AI that learns and interprets meaning and intent through sensorimotor experiences, enabling empathy and adaptable ethical reasoning.
Contribution
It introduces an agent model using enactivism and semiotics that learns meaning from sensorimotor data and infers intent, advancing ethical AI capabilities.
Findings
Agent can learn meaning of symbols through sensorimotor states
Agent infers intent and adapts its goals based on learned ethics
Mirror symbols enable empathy by linking own experiences with observed actions
Abstract
In order to construct an ethical artificial intelligence (AI) two complex problems must be overcome. Firstly, humans do not consistently agree on what is or is not ethical. Second, contemporary AI and machine learning methods tend to be blunt instruments which either search for solutions within the bounds of predefined rules, or mimic behaviour. An ethical AI must be capable of inferring unspoken rules, interpreting nuance and context, possess and be able to infer intent, and explain not just its actions but its intent. Using enactivism, semiotics, perceptual symbol systems and symbol emergence, we specify an agent that learns not just arbitrary relations between signs but their meaning in terms of the perceptual states of its sensorimotor system. Subsequently it can learn what is meant by a sentence and infer the intent of others in terms of its own experiences. It has malleable intent…
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