{"ID":2830757,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09729","arxiv_id":"2512.09729","title":"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.","short_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 evalua...","url_abs":"https://arxiv.org/abs/2512.09729","url_pdf":"https://arxiv.org/pdf/2512.09729v1","authors":"[\"Laurynas Adomaitis\",\"Vincent Israel-Jost\",\"Alexei Grinbaum\"]","published":"2025-12-10T15:10:42Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\"]","methods":"[]","has_code":false}