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 in public-sector AI
The evaluation of Allegheny County’s homelessness risk tool examined performance and potential disparities across protected groups. The insights led to stronger monitoring, clearer procedures, and better guidance for teams using the system in practice.
Audit of an AI-based wellbeing support application
Safety, fairness, and privacy were tested across sensitive, real-world scenarios involving an AI wellbeing companion. The review sharpened crisis-response protocols, reduced subtle bias, and reinforced safeguards throughout the user experience.
Responsible AI for wellbeing apps
An ethics and algorithmic review of two digital wellbeing apps identified key opportunities to strengthen data transparency, accessibility, and inclusivity. The assessment supported the integration of responsible AI practices throughout product development.
AI in Urban Safety: Challenges and Governance
Technology is pervasive in current police practices, and has been for a long time. From CCTV to crime mapping, databases, biometrics, predictive analytics, open source intelligence, applications and a myriad of other technological solutions take centre stage in urban safety management. But before efficient use of these applications can be made, it is necessary to confront a series of challenges relating to the organizational structures that will be used to manage them, to their technical capacities and expectations, and to weigh up the positive and negative external factors at play at the intersection between technology, society and urban management.