Artificial intelligence (AI) models are increasingly autonomous in decision making, making the pursuit of responsible AI more critical than ever. Responsible AI (RAI) is defined by its commitment to transparency, privacy, safety, inclusiveness, and fairness. But while the principles of RAI are transparent and shared, RAI practices and auditing mechanisms are still incipient. A key challenge is establishing metrics and benchmarks that define performance goals aligned with RAI principles. This paper presents how the ITACA AI auditing platform incorporates demographic benchmarking for AI recommender systems to identify and measure bias. We propose a Demographic Benchmarking Framework to measure populations potentially affected by specific models, set acceptable performance ranges, and guide policymakers and developers. Our approach integrates socio-demographic insights directly into AI systems, reducing bias while also improving overall performance. The main contributions of this study include: 1. Defining control datasets tailored to specific demographics so they can be used in model training to quantify sampling bias; 2. Comparing the overall population with those impacted by the deployed model to identify discrepancies and account for structural bias; and 3. Quantifying drift in different scenarios continuously and as a post-market monitoring of deployment bias.

Eticas provided technical support for the Support Pool of Experts Programme at the European Data Protection Board (EDPB), an initiative launched at the request of the Spanish Data Protection Agency (AEPD). The resulting methodology was officially published by the EDPB on June 27, 2024.

As part of this work, Eticas contributed an end-to-end socio-technical algorithmic audit (E2ESAT/AA) methodology — a framework for evaluating AI systems across both their technical performance and their social impact. The deliverables also included a practical AI auditing checklist for regulators and organizations to assess algorithmic accountability, a proposal for "AI leaflets" designed to give end users clear, accessible information about how automated systems affect them, and a proposal for algorithmic scores intended to communicate a system's risk or reliability at a glance.

Together, these tools give data protection authorities and the organizations they oversee a shared, repeatable approach to auditing AI systems for compliance and fairness.

This paper presents the evaluation (algorithmic audit) of REM!X, a personalized well-being recommendation app developed by Telefónica Innovación Alpha. The main goal of the evaluation was to identify and mitigate algorithmic biases in the recommendation system that could lead to the discrimination of protected groups.

The audit was conducted through a qualitative methodology that included five focus groups with developers and a digital ethnography relying on users comments reported in the Google Play Store. To minimize the collection of personal information, as required by best practice and the GDPR, the REM!X app did not collect gender, age, race, religion, or other protected attributes from its users. This limited the algorithmic assessment and the ability to control for different algorithmic biases. Indirect evidence was thus used as a partial mitigation for the lack of data on protected attributes.

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.

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.

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.

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.

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.