Ethics Readiness of Artificial Intelligence: A Practical Evaluation Method

TL;DR

This paper introduces Ethics Readiness Levels (ERLs), a practical, iterative method for integrating ethical reflection into AI system design through context-specific prompts, checks, and a dynamic questionnaire, fostering a shift towards ethics-by-design.

Contribution

The paper presents a novel four-level ERL framework that operationalizes ethical principles in AI development and provides a structured evaluation method with case studies.

Findings

ERLs effectively guide ethical integration in AI design.

The methodology promotes dialogue between ethics experts and engineers.

Case studies show ERLs lead to concrete ethical improvements.

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.