Ethics Readiness of Artificial Intelligence: A Practical Evaluation Method
Abstract
We present Ethics Readiness Levels (ERLs), a four-level, iterative method to track how ethical reflection is implemented in the design of AI systems. ERLs bridge high-level ethical principles and everyday engineering by turning ethical values into concrete prompts, checks, and controls within real use cases. The evaluation is conducted using a dynamic, tree-like questionnaire built from context-specific indicators, ensuring relevance to the technology and application domain. Beyond being a managerial tool, ERLs help facilitate a structured dialogue between ethics experts and technical teams, while our scoring system helps track progress over time. We demonstrate the methodology through two case studies: an AI facial sketch generator for law enforcement and a collaborative industrial robot. The ERL tool effectively catalyzes concrete design changes and promotes a shift from narrow technological solutionism to a more reflective, ethics-by-design mindset.
Keywords
Cite
@article{arxiv.2512.09729,
title = {Ethics Readiness of Artificial Intelligence: A Practical Evaluation Method},
author = {Laurynas Adomaitis and Vincent Israel-Jost and Alexei Grinbaum},
journal= {arXiv preprint arXiv:2512.09729},
year = {2025}
}
Comments
23 pages. Data available on GitHub at https://github.com/LA-NS/ethics-readiness-levels