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.
Smart Cities: Risks, Governance, and Surveillance Insights
The possibility of so-called ‘smart' technologies to improve city life has filled both pages of concern and PR leaflets. While the corporations driving these developments have emphasized how smart technologies can improve efficiency, critics have warned against the risks associated with the proliferation of smart surveillance. However, a critical discourse about the potential, limits and risks of the proliferation of smart technologies has not yet emerged, and in most instances public officials and decision-makers are ill-equipped to judge both the value and the externalities of the technologies being sold under the label ‘smart cities'. This paper presents a summary of smart solutions and definitions, and draws on the surveillance literature to address issues and risks related to the global drive to outsmart competing cities in a context of global governance.
Participation and decision-making power in citizen science
Citizen science is challenging professional researchers and their organizations to rethink the way they do science and connect with society. In any citizen science project, professional researchers are “making a promise” to the public about the level of participation and power in decision making that they are willing to provide to citizen scientists. Researchers should set expectations explicitly to ensure informed participation, trust, and motivation. Also, the design of tools for informed consent, information sharing, recognition, and privacy has to be adapted to the new power relations and distributed knowledge production.
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.
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.