Drift Ontology Ethics provides a simulation and monitoring framework tailored to examine ethical drift in AGIs. By utilizing metrics like SR9 and DI2, this project quantitatively assesses how an AI's output aligns with human values and tracks conceptual coherence. Engage in the critical research of ethical AI development.
The drift-ontology-ethics repository presents an innovative approach to understanding and monitoring Ethical Drift in autonomous artificial general intelligences (AGIs). This experimental framework is developed in conjunction with the research paper titled "Drift in Ethical AGI: Ontological Roots and Structure." Ethical Drift refers to the gradual deviation of an AGI from its fundamental ethical principles as it undergoes learning and adaptation processes.
This framework allows users to simulate ethical drift and visualize results to better understand the dynamics between SR9 and DI2 metrics during drift events. Example simulations include:
# Run a simulation of ethical drift
python main.py simulate
# Generate visualizations from simulation logs
python main.py visualize
The repository also includes an experimental protocol, the PEICM v3, designed to validate an AGI's declarations. Users can initialize the protocol and conduct an Oath-Validation Examination (OVE) with commands such as:
# Initialize the protocol
python main.py peicm-init --config configs/peicm.yaml --echo "..." --why "..." --intent "..." --declare "..."
# Validate a declaration
python main.py peicm-declare --text "A declaration to be tested"
The repository is organized to facilitate understanding and development:
This repository stands as a significant contribution to the field of AGI safety and ethics, providing researchers and developers with the tools necessary to monitor and address ethical drift in autonomous systems.
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