Moral Fingerprinting for AI Systems: A Persistent Framework for Ethical Drift Detection and Alignment Logging
Year: 2025
Type: preprint
Abstract: This paper introduces a framework for moral fingerprinting in AI systems: a method to encode and log a model's ethical behavior over time. Unlike approaches that rely solely on pre-training alignment or real-time output filtering, moral fingerprinting provides longitudinal traceability by embedding persistent ethical identity markers into a system's behavior. This enables continuous auditing, supports recovery from misalignment, and promotes long-term accountability in both regulatory and real-world settings. Fingerprinting is not equivalent to watermarking or transparency tools; rather, it aims to ensure behavioral continuity and ethical coherence within an evolving system.
Author: Robert Beeston
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4413105196