Eticas.ai has been evaluating AI systems in production since 2012. This is where we share what we’ve learned. You’ll find case studies from client work, practical guides on AI governance and evaluation, reports on emerging risks, interviews with practitioners, and a glossary of the concepts that matter most in the field. Whether you build AI, deploy it, or are accountable for its outcomes, this is the evidence base we work from.
AI Explainability
AI explainability is the degree to which an AI system's outputs can be understood by the people responsible for them and by those affected by them. In practice, this means being able to account for why a system produced a given output, which inputs influenced the result, and how confident the system was in its decision.