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.
Fairness Testing
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.