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.
Algorithmic Accountability
Algorithmic accountability refers to the obligation of organizations that develop or deploy AI systems to take responsibility for the outcomes those systems produce — and to be able to demonstrate that responsibility to regulators, affected individuals, and the public.
Model Drift
Fairness testing evaluates whether an AI system produces systematically different outcomes for different groups of people in ways that are unjustified, harmful, or legally impermissible.
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.
AI Risk Assessment
AI risk assessment is the process of identifying, measuring, and prioritizing the risks associated with an AI system before and during its deployment in production.