# Ethics Readiness of Artificial Intelligence: A Practical Evaluation Method

arXiv: https://arxiv.org/abs/2512.09729 · HTML: https://mlsanity.com/paper/2512.09729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Specific versus General Principles for Constitutional AI](https://mlsanity.com/paper/2310.13798/) (2023)
- [Technology Readiness Levels for AI & ML](https://mlsanity.com/paper/2006.12497/) (2020)
- [Ethics Readiness of Technology: The case for aligning ethical approaches with technological maturity](https://mlsanity.com/paper/2504.03336/) (2025)

## Abstract (arXiv)

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.