AI for Everyone
The AI for Everyone research program ensures that AI serves all people, regardless of background or circumstance. Our research develops fair, participatory AI by placing people, especially underrepresented communities, at the center of its design and deployment.
We create interactive platforms, novel data sampling and analysis techniques that reflect diverse populations while fostering sustainable, globally connected communities. By enabling people to co-design and use AI algorithms throughout the entire development cycle, we shift the paradigm from passive users to active participants. Our work also advances inclusive public discourse through innovations in collaborative decision-making and electronic voting, reimagining AI as a tool for transparent, interpretable, and inclusive societal progress.
People
Ongoing projects
Artificial Intelligence (AI) has become a powerful and pervasive technology in recent years, influencing numerous aspects of our daily lives. We encounter AI through recommendation algorithms in online stores, voice-activated smartphone assistants, or the widespread use of technologies like ChatGPT. However, its rapid growth and integration into society raise complex questions and concerns among the public. Public opinions on AI vary widely; while some people are enthusiastic about its potential to revolutionize industries and enable breakthroughs in fields such as medicine, others fear that AI could lead to undesirable outcomes, such as a loss of human control or privacy. These views are often shaped by media narratives and the competing interests of different stakeholders, which play a significant role in influencing both public opinion and policy decisions.
Our team of scientists and communication experts aims to enhance the understanding of AI technologies among the Swiss people, with a particular focus on teenagers and female students, to create a positive societal impact. Building on the foundations of our previous project, NewsOnAI, we will expand beyond traditional media such as newspapers and employ diverse methods, including artistic performances and interactive exhibitions. We will design these activities to be highly interactive, encouraging active participation and dialogue. Activities will include themed theater plays that explore AI’s impact on everyday life, exhibitions where participants can interact with AI tools, and workshops specifically designed for teenagers and female students to discuss AI’s future role in society. Feedback collected will include real-time audience reactions, structured questionnaires, and focus group discussions, which will be analyzed to continuously refine and adapt our engagement strategies.
Our primary audience includes Swiss citizens interested in cultural activities, particularly teenagers who are keen to follow new trends. Additionally, we are committed to addressing gender aspects by designing content and activities that specifically appeal to female students. We aim to inspire and empower young women to take on more prominent roles in shaping the digital world, acknowledging that they have historically been underrepresented in these fields. As societal attention shifts toward greater inclusion, our project will contribute to fostering a more balanced and equitable digital future.
While many individuals in our target groups may lack in-depth technical knowledge of AI, they often encounter new AI products, companies, and social issues through various media channels, including newspapers and science fiction movies. As a result, they may be aware of recent developments but also susceptible to misunderstandings and controversies related to technologies such as ChatGPT, Elon Musk's brain-chip startup, and other emerging AI applications. It is crucial to recognize that media portrayal significantly influences public opinion on AI, both positively and negatively. Media creators, even if they are not experts in AI, often produce content that captures public attention, which high-profile figures, including entrepreneurs, CEOs, and politicians, may leverage to advance their agendas. This can sometimes lead to skewed public perceptions, whether intentionally or unintentionally. Given this landscape, it is essential for AI scientists to collaborate with media creators, providing evidence-based insights to ensure accurate and balanced information is shared with the public. Our project fosters such collaboration, ensuring that both the potential and limitations of AI are clearly communicated. By sharing our findings through diverse media outlets, we aim to reach a broad audience, extending beyond Switzerland. Furthermore, our proactive engagement efforts will foster dynamic, two-way communication between scientists and the public, using interactive methods in exhibitions and theater plays to engage teenagers and female students specifically. Analyzing the feedback from these initiatives will provide invaluable insights into public perspectives on emerging technologies. This understanding will guide scientists in pursuing research directions that effectively address societal concerns, demonstrating the tangible benefits of our project for both scientific advancement and societal well-being. We anticipate that our efforts will have a multiplying social impact over time, promoting informed public discourse and a deeper understanding of AI technologies.
Au cours des vingt dernières années, les robots ont progressivement quitté les environnements industriels classiques pour entrer dans les espaces de vie et de travail des humains. De nouveaux robots ont ainsi été conçus spécifiquement pour interagir avec des personnes, ce qui implique des capacités avancées de manipulation, de déplacement autonome, de programmation intuitive, et d’interaction sociale afin d’être réellement utiles et acceptés par leurs utilisateurs. Ces évolutions ont donné naissance à la robotique d’assistance, un domaine de recherche qui étudie comment les robots peuvent soutenir les humains dans des contextes variés, allant de l’aide aux personnes âgées ou en situation de handicap jusqu’au soutien des travailleurs en entreprise. Toutefois, cette recherche pose des défis importants, notamment l’implication de patients et d’utilisateurs réels dans la conception des technologies, ainsi que la création d’environnements de développement et d’évaluation qui reproduisent fidèlement les conditions réelles de vie et d’activité. Pour renforcer le réalisme et l’impact de ses futurs projets, l’Idiap souhaite développer un nouveau Centre de Robotique d’Assistance, destiné à centraliser les recherches en robotique de l’Idiap, à recréer des conditions d’usage proches du réel, à accueillir des participants locaux et à mieux communiquer les avancées scientifiques auprès du public.
We live in a crucial historical moment, with tremendous challenges ahead, from climate change to the energy crisis. ELIAS emerges from the belief that AI will be a key discipline to help us tackle these challenges. At the same time, the development of AI entails deep ethical and societal concerns that need to be addressed. As for fundamental research, ELIAS will address key scientific questions
about how AI can reduce computational costs, serves to model effects of policy decisions on society, and impacts individuals. ELIAS will strive for a deep integration of the fundamental research that takes place in academia and the more applications-focused research from industry.
ELIAS builds on and expands the highly successful and internationally recognized European Laboratory for Learning and Intelligent Systems (ELLIS). ELIAS will further develop the excellence criteria and the pillars in ELLIS and implement actions that will support AI researchers and young talents at different stages of their careers. Furthermore, ELIAS will develop a Sciencentrepreneurship track, with the purpose of attracting and empowering talents at the interface of scientific innovation and business and establish original AI solutions that move towards a sustainable long-term future for our planet,
contribute to a cohesive society, and respect individual rights.
The outcome of ELIAS will be to establish Europe as a leader in AI research in which impact on the environment, society and the individual are integral considerations during development. We will measure the success of this endeavor in terms of key indicators, including the number of new cross-institutional collaborations, the number of cross-disciplinary collaborations, the number of
industry-academic partnerships, publications in top conferences and journals, patents, and the number of projects that have resulted in deployed technologies.
The algorithmic bias remains one of the key challenges for the wider applicability of Machine Learning (ML) in healthcare. Statistical modeling of natural phenomena has gained traction due to increased representation capacity and data availability. In medicine, particularly, the use of ML models has increased significantly in recent years, especially to support large scale screening, and diagnosis. However impactful, the study of demographic bias of newly developed or already deployed ML solutions in this domain remains largely unaddressed. This is particularly true in the medical imaging domain, where it remains challenging to associate demographic attributes with features. Out of the most recent results, the "impossibility of fairness" establishes some criteria for demographic impartiality cannot be reached simultaneously. Among other factors, the lack of raw data, in particular for intersections of minorities, is one of the greatest issues that remain unaddressed due to their challenging nature. This proposal addresses three important challenges in the domain of ML fairness for medical imaging: (i) Create novel ways to train ML models for medical imaging tasks, that can be automatically adjusted to become more useful (maximize performance), group or individually fair, (ii) Quantify fairness boundaries of ML models and associated development data, and finally, (iii) Build systems whose joint performance with humans in the decision loop is fair towards various individuals and demographic groups. To achieve these goals, we will develop a novel evaluation framework and loss functions that take into account model utility together with all aspects of demographic fairness one may wish to address. A generative framework, trained to isolate tunable demographic features, will provide large-scale data simulation covering minorities and intersections. We will then study fairness (safety) boundaries through a modified learning curve setup, analyzing and quantifying limits in both ML models and training data. Finally, we will study how humans-in-the-decision-loop affect the fairness of hybrid human-AI systems, and address post-deployment utility/fairness tuning by embedding weight coefficients directly into the trained model. The development of methods and tools to detect, mitigate, or remove bias will improve the safety of ML models deployed in healthcare. We expect our work will help define new operational boundaries for the responsible deployment of artificial intelligence tools.
Past projects
Motivated by the challenges, risks and opportunities that the wide use of AI brings to media, society and politics, AI4Media aspires to become a centre of excellence and a wide network of researchers across Europe and beyond, with a focus on delivering the next generation of core AI advances to serve the key sector of Media, to make sure that the European values of ethical and trustworthy AI are embedded in future AI deployments, and to reimagine AI as a crucial beneficial enabling technology in the service of Society and Media. The AI4Media consortium, comprising 30 leading partners in the areas of AI and media (9 universities, 9 research centres, 12 industrial partners) and 35 associate members, will establish the networking infrastructure to bring together the currently fragmented European AI landscape in the field of media, and foster deeper and long-running interactions between academia and industry, including Digital Innovation Hubs. It will also shape a research agenda for media AI research, and implement research and innovation both with respect to cutting-edge technologies at the core of AI research, and within specific fields of media-related AI. AI4Media will provide a targeted funding framework through open calls, to speed up the uptake of innovations developed within the network. A PhD programme will further enhance links to the industry and the fostering and exchange of talent, while providing motivation to prevent brain drain, and a set of use cases will be developed by the network to demonstrate the impact of the achieved advances in the media sector. The Excellence Centre that is established during the AI4Media project, and the ecosystem that will grow around it, will provide a long-term basis for the support of AI excellence in Europe, long after the project end, with the aim of ensuring that Ethical AI guided by European values assumes a global leading role in the field of Media.
Background and Motivation. The HEP-VS is a key academic institution for the education of children, adolescents, and young adults in Valais. The potential transformation that AI will enable in education is unprecedented, as AI technologies open opportunities but also involve significant risks. To incorporate AI as part of present and future educational programs, and effectively serve the multiple actors in the cantonal educational ecosystem – including HEPVS students, HEPVS faculty and instructors, teachers and students in public schools - a systematic institutional approach to acquire AI literacy (knowledge, skills, and abilities needed to effectively interact with AI technologies) is paramount.
In parallel, Idiap, as a prominent institution in AI research, has been committed to inventing core advanced technologies, but also to design methods to facilitate the widespread understanding and use of these technologies, aligned with its “AI for Society’ mission. In particular, Idiap has recently developed a conceptual framework for AI Literacy, based on twelve competencies that span a range of needed knowledge, skills, and abilities, and focused on last-generation Generative AI technologies.
HEP-VS and Idiap started discussions in 2024 to envision a long-term collaboration as key cantonal institutions in education and AI, with the ambitious objective of devising a systematic approach to support the HEP-VS reflect upon, acquire, and spread AI Literacy, based upon the HEP-VS’s specific needs and priorities, and contextualized with different actors of the cantonal educational ecosystem.
Objective and methodology. The proposed one-year project presented here represents both a launching pad and a proof-of-concept of the Idiap/HEP-VS long-term collaboration. We will develop the systematic approach based on the twelve-competency Generative AI Literacy Framework and an “AI Literacy & Literacy with AI” approach that will integrate pedagogical, participatory, and technological methods. The project includes specific research activities to achieve 3 research objectives: (O1) assessment of the state of AI literacy for a population of HEP-VS students; (O2) co-design of customized educational modules for key components of the Generative AI Literacy Framework; and (O3) prototyping and initial testing of such modules. Importantly, some of the prototyping will use state-of-the-art AI as a tool to support AI literacy acquisition and assessment, thus integrating the expertise of Idiap in NLP and social computing, while HEP-VS will contribute to the research framework through its expertise, particularly in the areas of methodological design (educational sciences and disciplinary didactics), pedagogical design and experimentation (digital learning engineering and pedagogical facilitation), as well as in the analysis of the effectiveness and efficiency of classroom interventions and the evaluation of their implementation (statistics, cognitive psychology, epistemology of scientific inquiry, etc.). The conceptual framework proposed by Idiap for Gen AI literacy is directly aligned with the issue of effectiveness (learning outcomes) and efficiency (resource optimization) in the use of AI for learning across distinct disciplinary domains.
Expected outcomes and impact. As research outputs, the project will produce an assessment of the state of AI Literacy of a sample of HEP-VS students; educational resources for Generative AI Literacy (both consolidated from existing ones, as well as novel resources developed in the project); and joint scientific publications. Furthermore, the impact beyond concrete research outputs includes the consolidation of the bi-institutional partnership about this important priority theme for both HEP-VS and Idiap, and the establishment of first joint scientific results, which are needed to apply for larger research and educational projects.
Collaborative breakthrough research in Artificial Intelligence (AI) requires access to well-tuned systems, specific computing, large datasets, and dedicated storage, often only accessible to a select few. This reality slows down complementary research, as a substantial amount of project time is often dedicated to repeating setups, understanding computation and storage intricacies, data properties, and how to properly transfer systems between institutions, to pursue overall objectives. Examples can be often found in cases encompassing consortia with computational versus non-computational specialists, or containing a virtuous mixture of domain experts (medical doctors, biologists, psychologists) and data scientists. Reproducibility and technology transfer are essential tools in thriving projects, however these concepts are costly to implement and maintain. Deploying AI solutions requires multidisciplinary expertise, the right hardware, and AI specialists to tune and ready tools for collaborative use. In practice, lack of specific expertise slowdowns partner-to-partner communication affecting overall productivity. In projects with industry or government-academia partnerships driven by concrete societal needs, replicating project conclusions with different, private datasets, or allowing partners to infer from pre-trained models using adequate hardware setups, is often avoided because of these barriers. Moreover, the growing scale, complexity and impact of contemporary AI systems such as Large Language Models (LLMs) accelerates the need for accessible infrastructures which can guarantee systematised, transparent and increased collaborative work. We intend to bridge these gaps by building “CollabCloud", a cloud-based research infrastructure to boost collaborative research for current and future projects at the Idiap Research Institute. The main focal points will be boosting the ability of easily exporting researchworkflows, exploring AI models by both computational and non-computational experts, and allowing controlled access to shared storage and computing power for collaborative projects. Beyond these goals, CollabCloud will enable Idiap to participate in developing important topics shaping the future of AI, such as Federated Learning, and cloud-based scientfic networks, which require connectivity and storage capabilities adapted to such purposes. As discussed in the institutional support letter, this vision aligns well with Idiap’s future, its predicted growth (with a current data center reaching the limits of its maximum capacity), and the notion of Cross Research Groups (CRG), that is part of our 2021-2024 Research Program as approved by the Federal State Secretariat for Education, Research, and Innovation (SERI).
Ziel dieses Projekts ist das Erstellen eines multilingualen Eyetracking-Korpus, das Blickbewegungsdaten beim Lesen von Texten in typologisch verschiedenen sowie verwandten Sprachen enthält. Es sollen slawische Sprachen (Kroatisch, Mazedonisch, Russisch, Slowenisch), romanische Sprachen (Französisch, Portugiesisch, Rätoromanisch, Spanisch), germanische Sprachen (Dänisch, Deutsch, Niederländisch, Englisch), baltische Sprachen (Lettisch, Litauisch), eine semitische Sprache (Maltesisch), eine finno-ugrische Sprache (Estnisch) sowie das keiner Sprachfamilie zugehörige Baskische enthalten sein. Das Korpus soll teilweise aus parallelen (übersetzten) und teilweise aus originalen – jedoch in Inhalt und Register vergleichbaren – Texten aufgebaut werden. Das Korpus soll derart aufgebaut sein, dass es als Ressource sowohl zur sprachübergreifenden psycholinguistischen Erforschung der menschlichen Sprachverarbeitung als auch zur Weiterentwicklung maschineller Sprachmodelle herangezogen werden kann. Die oben beschriebenen Limitationen bestehender Datensätze sollen vermieden werden. So sollen begleitend zu den Eyetracking-Experimenten psychometrische Tests durchgeführt und demographische Daten erhoben werden und bei der Auswahl der Proband*innen soll auf die demographische Ausgeglichenheit der Stichprobe geachtet werden. Im Bereich der Psycholinguistik soll der Datensatz erstmals die Möglichkeit schaffen, bestehende Theorien sprachübergreifend zu evaluieren sowie neue Theorien direkt aus der cross-linguistischen Perspektive heraus zu entwickeln, um somit eine höhere externe Validität zu erreichen als bisherige, ausschliesslich auf Basis des Englischen entwickelte Theorien zur menschlichen Sprachverarbeitung. Im Bereich der Sprachtechnologie soll der Datensatz es ermöglichen, state-of-the-art neuronale Sprachmodelle intrinsisch auf deren kognitive Plausibilität hin zu evaluieren, deren Interpretierbarkeit zu verbessern (cognitively explainable NLP) und ihr Verhalten natürlicher (menschenähnlicher) und somit akkurater zu machen (cognitively enhanced NLP). Der Datensatz soll weiterhin dazu dienen, technologische Lösungen zu finden, so dass kleinere Sprachen am sprachtechnologischen Fortschritt teilhaben können (cognitively enhanced transfer learning).
