OntoMotoOS: A Value-Based Logical Safety Filter for Risk Detection ( Part 2 of the OntoMotoOS conceptual framework series)

OntoMotoOS is a value-based logical safety filter designed to detect and flag ethical risks in ideologies, digital communities, and AI-generated content. This three-part series presents the theoretical foundations, practical applications, and critical evaluation of OntoMotoOS.

Drawing on philosophical principles from Kant, Mill, Rawls, and Habermas, OntoMotoOS integrates three key dimensions—Directionality, Freedom, and Universality—into a formal Boolean logic model. The system offers a practical tool for identifying manipulative, authoritarian, or potentially harmful patterns in both human-led and machine-generated discourse.

Part I outlines the conceptual framework and philosophical justification for the filter.

Part II applies the model to real-world and AI-generated case studies, demonstrating its utility for risk assessment.

Part III critically reviews the model's limitations, addressing issues of cultural bias, value pluralism, and the need for context sensitivity.

OntoMotoOS aims to advance applied ethics, AI safety, and digital governance by providing a transparent and operational framework for risk detection in complex socio-technical systems.