Ongoing Projects
Currently, we are engaged in projects focused on advancing research in Artificial Intelligence (AI), particularly in Trustworthy AI, AI & Ethics, and Applied AI in Data for Good, Social Science and Healthcare. Specifically, we are working on the following projects:
- Exploring Large Language Models (LLMs) Alignment Through Personality Psychology.
- AINarratives: Enhancing Chronic Pain Assessment and Treatment through AI-aided Narratives.
- Advancing Social Science with Synthetic Populations via Large Language Models (LLMs).
- Industrial Doctorate: Exploring the Impact of AI on Political Candidates News
- Industrial Doctorate: AI for Disaster Risk Management
Exploring Large Language Models (LLMs) Alignment Through Personality Psychology
Due to their remarkable proficiency in natural language conversation and reasoning, LLMs are revolutionising human-computer interaction. They are increasingly permeating our personal, professional, and social lives, significantly advising people on individual or collective decisions with real-world implications. This evolution prompts the critical question of how well LLMs align with human judgment and values. Taking into account research in psychology which characterises judgment and values in the context of personality, in this project, we propose to tackle the question of LLM alignment through the lens of personality psychology. In other words, we aim to explore the extent to which insights from personality psychology can be applied to fine-tune LLMs’ personalities to address the alignment problem.
AINarratives: Enhancing Chronic Pain Assessment and Treatment through AI-aided Narratives.
Chronic pain poses a widespread challenge, affecting approximately 25% of the global population. Affecting people’s daily activities and quality of life, chronic pain significantly contributes to the demand for medical services, imposing a noteworthy economic burden on both individuals experiencing the pain and society at large. Accordingly, improving assessment tools is crucial to understanding pain experiences and designing effective interventions.
This project aims to improve pain assessment processes by combining qualitative approaches to pain assessment and the use of artificial intelligence (AI). Although standardized questionnaires are effective in gathering information about individuals, they may not capture the full spectrum of chronic pain experiences. Qualitative methods – for example, written narrative (WN) -, can help overcome these limitations by offering a deeper understanding of a subjective experience such as pain.
Although WN are valuable tools for assessment they can be time-consuming for clinicians and challenging for patients to produce. This project aims to streamline this process by helping patients articulate their pain experiences and aiding clinicians in their evaluations. More precisely, in this project, we will use AI, specifically large language models (LLMs), to make it easier for people with pain to narrate their experience, while facilitating a quick assessment of the same for professionals. To achieve this, we will develop AINarratives, a platform where people can express their pain through writing or speech. AINarratives will ask people for additional details to help them explain, but will also automatically evaluate the content communicated and provide summary information in relation to different parameters relevant to practitioners. This information will empower professionals to explore in more depth the essential elements highlighted in the writings and formulate interventions aimed at the expressed needs.
Advancing Social Science with Synthetic Populations via Large Language Models (LLMs)
Questionnaires and surveys are efficient research methods for acquiring information about individuals, especially valuable for uncovering details that are not directly observable or measurable. They are particularly useful for gathering data on people’s subjective experiences. These tools can collect views on a wide range of topics, such as marketing research, customer satisfaction and service performance, political vote orientation, healthcare services utilisation or usage intention, opinions about a service or new procedure/intervention, and the occurrence of health issues for epidemiological purposes. Given their applications, efficiency in data collection, and low cost, surveys and questionnaires are widely used in Social Science.
Designing effective surveys and questionnaires involves more than just assembling a series of questions. Attention must be paid to the overall structure, flow, coherence, and relevance of the questions. The two most critical and time-consuming steps in this process are the pre-test (or pilot test) and the assessment of psychometric properties, such as reliability and validity. These steps are complex, requiring the computation of multiple indices and the application of the questionnaire to diverse populations.
In light of these challenges, this project aims to explore the potential of using Large Language Models (LLMs) to assist researchers and healthcare professionals in simulating populations for testing surveys and questionnaires. Specifically, we seek to develop strategies—both prompt engineering and few-shot training —to leverage LLMs for this purpose. Our goal is to facilitate the testing and development of surveys and questionnaires, thereby supporting researchers and healthcare professionals in their work.
Industrial PhD: Exploring the Impact of AI on Political Candidates News:
Artificial Intelligence models, such as Recommender Systems (RS) and Large Language Models (LLM), are increasingly mediating access to political information, raising concerns about their impact on democracy, particularly during elections. However, despite the growing body of research on AI systems, a cross-platform and cross-country comparison of the impact of different AI systems within the same election is still missing. This project seeks to investigate the effects of RS and LLM on the news landscape using the 2024 EU elections as a case study.
Focusing on YouTube and TikTok’s RS and various LLMs, the research will examine how prominent politicians are presented in different nations and platforms. Additionally, the project will assess potential biases, risks, and legal implications for European Regulations.
By employing a combination of algorithmic auditing techniques and digital methods to collect the data and a combination of computational methods/techniques to analyze them, the research aims to provide insights into how AI systems shape the diffusion of political candidates’ information and offer recommendations for assessing compliance with mandatory Risk Assessment and independent audits outlined in the Digital Services Act. The findings from this study will be presented through a series of scholarly papers and a final thesis, with the goal of fostering discussion and collaboration across academia, industry, and regulatory bodies.
Industrial PhD: AI for Disaster Risk Management
Anticipation of weather impacts with Artificial Intelligence techniques
In the context of Climate Change, extreme weather-induced events are expected to increase in frequency and intensity. In this sense, Early Warning Systems (EWS) have been identified as a crucial instrument to trigger actions supporting situational awareness and rapid response in front of weather-related emergencies. International organizations (such as thw UN, WMO, and UNDRR) are promoting the development and implementation of Impact-based EWS (IEWS) adapted to the local needs of authorities, first responders and the population. IEWS are oriented to anticipate the actual impact of extreme weather on people, infrastructures and critical services and elements than the meteorological extremes themselves. Artificial Intelligence and Machine Learning techniques can identify patterns in meteorological data that may relate to impacts with higher anticipation than traditional methods. In this project, highly detailed real-life impact data as well as a wide variety of meteorological inputs will be used to the recognition of these types of patterns and to provide significant improvement in impact early warning.