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.
Auditing AI Enabled Career Advisory Platform
The independent audit by Eticas.ai of Career Scoops AI-enabled career advisory platform for students (ages 13+) was performed post-deployment and focused on the end users in real educational settings. The findings confirmed a safe, reliable and student-appropriate deployment of AI for career exploration, and pointed to a few recommendations for improvement.
Deep Learning for social services
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.
Detecting bias in AI hiring systems
The FINDHR project introduced practical tools, guidelines, and auditing frameworks designed to reduce discrimination in AI-assisted hiring. These resources support more transparent, inclusive, and accountable recruitment practices across Europe.
Auditing an AI-based cybersecurity application
This assessment of a high-risk cybersecurity model revealed critical gaps in data quality, governance, and transparency. The work strengthened compliance with the EU AI Act and GDPR while significantly improving the system’s accuracy, fairness, and reliability.
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.