Metacognitive Agents for Ethical Decision Support: Conceptual Model and Research Roadmap

TL;DR

This paper proposes a conceptual framework and research plan for developing metacognitive agents that leverage cognitive-affective models to reduce the ethical value-action gap in decision-making.

Contribution

It introduces a novel research roadmap for translating cognitive-affective models into ethical decision support agents with metacognitive capabilities.

Findings

Metacognitive agents can potentially improve value-aligned decision-making.

Cognitive-affective models offer insights into reducing the value-action gap.

A structured research roadmap guides future development of ethical AI agents.

Abstract

An ethical value-action gap exists when there is a discrepancy between intentions and actions. This discrepancy may be caused by social and structural obstacles as well as cognitive biases. Computational models of cognition and affect can provide insights into the value-action gap and how it can be reduced. In particular, metacognition ("thinking about thinking") plays an important role in many of these models as a mechanism for self-regulation and reasoning about mental attitudes. This paper outlines a roadmap for translating cognitive-affective models into assistant agents to help make value-aligned decisions.