The Scientific Employment Stimulus Programme (Concurso de Estímulo ao Emprego Científico Individual, or CEEC Individual) is a competitive research-employment programme funded by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia, FCT). It is designed to attract and retain highly qualified researchers in Portugal, strengthen the national scientific system, and create employment opportunities for PhD holders. Unlike a conventional research fellowship, CEEC is specifically structured around the employment of researchers by host institutions, with funding provided by FCT.
The programme supports individual researchers who propose an original research project to be conducted within a Portuguese university or research institution. Applicants are evaluated through an international peer-review process, which assesses the researcher’s scientific merit, research and career-development plans, and the quality and suitability of the proposed research environment. The programme therefore combines competitive individual selection with the objective of strengthening the research capacity of Portuguese institutions.
CEEC Individual distinguishes between different research-career categories, including Investigador Júnior (Junior Researcher) and Investigador Auxiliar (Assistant Researcher). The latter is a career-level research category intended for experienced PhD holders with an established scientific record. Under the programme, FCT finances the employment contract corresponding to the awarded category, while the host institution is responsible for employing the researcher. The resulting appointment provides a formal research position rather than merely a personal grant or stipend.
From a funding perspective, CEEC Individual supports researchers through employment contracts financed by FCT. Under the eighth edition, funding is available for up to three years of employment, subject to the programme’s applicable conditions. Researchers benefit from an employment relationship with the host institution, with the corresponding employment rights and social-security arrangements governed by the applicable Portuguese legal framework.
In academic signalling terms, a CEEC Individual award is a competitive national research-employment distinction and a strong indicator of scientific merit within the Portuguese research system. The selection process is highly competitive, with applications assessed and ranked by international scientific panels. An award at the Investigador Auxiliar level is particularly significant because it places the researcher within an established research-career category, rather than a generic postdoctoral fellowship scheme. Although the contract is not automatically permanent and does not confer the separate university teaching rank of Professor Auxiliar, it can provide a foundation for subsequent research leadership, further competitive funding, and applications for permanent research positions.
In 2026, I was awarded a CEEC Individual research-employment contract at the Investigador Auxiliar level, following the international evaluation of my application in the scientific area of Law. I ranked first among 73 applicants in my evaluation panel, with only four candidates selected. The award will support my research at the LASIGE research unit, within the scientific environment of Ciências ULisboa.
THE MEDAID PROJECT
The integration of AI into clinical practice brings clear advantages but also some significant risk. Because AI systems learn from historical data, their accuracy and reliability depend on how representative and well-governed those data are. Biased, incomplete, or wrong datasets can propagate errors and influence treatment recommendations negatively, with direct consequences for patient safety. In my Marie Skłodowska-Curie project, DataCom, I examined how the EU Digital Strategy, in particular the European Health Data Space (EHDS), the AI Act (AIA), and the Data Governance Act (DGA), reshapes health data sharing and reuse. The EHDS establishes a mandatory cross-border infrastructure to enable widespread secondary use of electronic health data across the EU (research institutions, public agencies, hospitals, companies, etc.). The AIA classifies medical AI as “high risk,” introduces stringent data-governance duties to address bias, and creates a new legal basis for health-data reuse. In addition, the Data Governance Act’s data-altruism mechanism is also conceptualized as to expand access to high-value datasets. Once fully implemented, these instruments will dramatically increase the volume and circulation of health data, especially for AI applications.
However, legal scholarship has yet to analyze how these instruments interact in practice, how their novel obligations will operate at the point of dataset creation, and what concrete consequences they may have for fairness and non-discrimination. Medicine has long been showed to embed structural biases (e.g., the underrepresentation of women and other marginalized groups in research and care) leading to unequal outcomes. AI can amplify these disparities if upstream data-collection practices remain unexamined. Much of the current debate centers on technical debiasing or post-hoc fixes, while the fairness of data collection itself, and its alignment with ethical and legal norms, remains underexplored.
This project is aimed at addressing that challenge. It investigates how clinical practices, institutional arrangements, and regulatory requirements shape the composition, validity, and equity of medical AI datasets. Using an anticipatory regulation approach and feminist ethnography, the project will:
(a) construct a theoretical framework for anticipatory regulation that systematically maps where and how biases emerge during AI dataset creation in healthcare;
(b) examine health dataset-creation practices through feminist ethnography to surface power relations and biases related to gender, sexual orientation, ethnicity, religion, age, health, and socio-economic status;
(c) design and test remedial and mitigation protocols, co-created with clinicians, patients, and other stakeholders, to improve fairness in medical dataset creation and evaluate their efficacy;
(d) formulate EU-relevant policy recommendations to embed fairness, non-discrimination, lawfulness, and ethics into digital and health data legislation governing AI dataset creation.
RESEARCH PLAN AND METHODS
The work plan has been designed to ensure the achievement of its objectives within 36 months:
– WP1 will evaluate the AI Act, EHDS, and MDR provisions with a postcolonial and feminist lens. Examining existing AI datasets from participating organization in Portugal (Braga Hospital, LAGISE, and NOVA School of Medicine), as well as national legislation related to AI and health data, focusing on data governance practices, we will conduct a critical analysis aiming to identify gaps in regulatory frameworks that fail to address postcolonial and intersectional issues in AI dataset creation. Special attention will be given to how marginalized patient groups are put at risk by existing legislation and lack thereof. Employing postcolonial analysis, we will critique how current regulations may reinforce historical power imbalances. Employing intersectional perspectives, we will identify how policies impact individuals with multiple marginalized identities.
Guiding questions: In what ways do colonial legacies influence current data practices and policies related to AI in healthcare? How does the lack of clear definitions of “bias” in legislation affect the understanding and mitigation of discriminatory effects in AI systems? How do the AI Act, EHDS, and MDR define and address “bias” in AI datasets, and what are the implications of their definitions or lack thereof? In what ways might current regulations reinforce historical power imbalances affecting marginalized groups? What gaps exist in addressing intersectional discrimination within legislative frameworks?
In order to do so, I will employ a methodology that I developed during the RAISE project and the MSCA fellowship. First, I will start from the analysis of specific EU legal provisions (e.g., art. 4a and 10 of the AI Act). After that, I will proceed with a systematic legal interpretation of the provision and its intersection with other relevant laws and regulations (such as specific provisions of the EHDS, internal medical law, etc.). I will then analyze how those findings relate to the actual practices carried out in hospitals during the dataset creation process to understand what issues related to power imbalances might arise and what impact this might have on marginalized people. For example, in my recent MSCA preprint, I analyzed how the secondary use provisions from the EHDS and AI Act allow for a new form of data colonization by hi-tech fertility clinics, that exploit women in the Global South to harvest data with the excuse of enriching their datasets for AI debiasing.
– WP2 will critically analyze existing AI datasets used in clinical settings to identify embedded biases. Examining existing AI datasets and data practices from participating organizations, we will conduct a critical analysis of the data points by comparing them with the information in Electronic Health Records and by conducting interviews with patients. The critical review of dataset composition will focus on the representation of different social groups and identifying potential biases related to gender, race, and other intersectional identities, with special attention will be given to how marginalized patient groups are represented in the datasets.
Guiding Questions: How are various social groups represented in the AI datasets compared to their actual demographics in EHR? What are the sources of biases in the dataset creation process, and how do they relate to historical and cultural contexts? In what ways do power relations between data collectors, data scientists, and marginalized groups affect dataset formation? In what ways have local cultural norms influenced the representation of marginalized groups in datasets? What patterns of errors and omissions can be identified in data, and how do they affect current datasets?
In WP2 we will (passively) observe and interview data scientists who receive the datasets from hospitals and preprocess them and we will critically analyze such datasets. We will first select three case studies and then we will examine how the related datasets are received, handled, cleaned, and structured. The case study methodology is important as it represents “a better opportunity to gain detailed knowledge of the phenomenon under investigation”. The case studies will be selected prioritizing the diverse types of data used in AI (e.g., structured, unstructured, images, textual, Boolean, etc.), the technique used (deep learning, LLM, fuzzy logic, random forest, Bayesian methods, etc.), and the problem to be solved (classification, regression). This will let us understand if biases in data collection and transcription are identified by data preprocessors and if new biases are introduced in the dataset. After the datasets are preprocessed and considered ready to be used in AI (in the perspective of the data preprocessor), they will be analyzed by two research assistants who are experts in data science and trustworthy AI. Particular attention will be paid to patients excluded from healthcare and from datasets (e.g., no access to healthcare, discriminatory practices, material errors). This analysis will be informed by postcolonial theory to assess how historical inequities are perpetuated through data practices, while intersectionality will guide the exploration of how multiple forms of discrimination are encoded within datasets. Intersectionality is indispensable in the analysis of clinical datasets as it provides a nuanced understanding of how overlapping social identities can lead to different kinds of biases.
To triangulate the results, a retrospective analysis on three selected datasets will also be carried out to check whether digital health records match the information in Patient Health Records and, if not, to what extent they differ, and how many transcription errors are present. The analysis will focus on PHR and EHR used in the past in each institution to create an AI dataset. Such old datasets will also be analyzed as explained above to unveil the nuanced ways in which data practices related to dataset creation shape and are shaped by gender relations and other types of power imbalances.
WP3 will integrate data from previous WPs, synthesizing findings to provide an integrated understanding of biases in AI datasets. The synthesis will thus involve integrating qualitative insights from ethnographic fieldwork with quantitative findings from dataset analysis and legal gap analysis. This integration entails a qualitative analysis aiming to provide a holistic view of how biases manifest in AI datasets and their legal contexts. Through this synthesis, we will ultimately develop a novel theory based on the critical analysis of datasetification. Based on this analysis, guidelines for policymakers will be developed and co-created with patients and affected stakeholders.
WP 4 Dissemination and Communication: this work package is dedicate to disseminate the project findings within the academic community and to communicate it to the general public and other stakeholders.
WP 5 Project management: this work package is dedicated to the administrative, financial, and bureaucratic aspects of the project.
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