MDRAI → Master in Design for Responsible Ai

In this track we develop a collaborative research project to investigate how metaphors influence the ways AI is understood, communicated, and designed. Participants explore past and present social meanings of AI metaphors to imagine alternative ones that align with more equitable, ecologically aware, and culturally grounded visions of the concept of artificial intelligence.

In this course we develop the practice of cultivating a personal online space for ideas to grow, evolve, and interconnect over time. Through hands-on exercises, collaborative exploration, and reflective writing, participants learn how to organize knowledge, build creative workflows, and design digital ecosystems that support curiosity, learning, and long-term thinking.

In this course we engage with readings that critically examine key underlying concepts of AI through artistic, cultural, and philosophical lenses. Participants write essays individually and collaboratively, exploring how artists and thinkers interrogate AI’s aesthetics, politics, and impacts in social imaginaries. Discussions and shared reflections deepen understanding and foster creative, critical communication of insights.

In this course we explore AI from a socio-technical perspective, examining the design, functioning, use, and lifecycle of technology. Through reflection and analysis, they consider the diverse social, cultural, and technical dimensions that shape how these systems operate and impact society.

In this course we explore our relationship with time as a hidden axis of AI, design, and society. Participants examine multiple temporalities, trace AI’s material and social rhythms, and learn from afro-diasporic and Indigenous perspectives. Through reflection, case studies, and speculative exercises, participants develop tools for integrating temporality into responsible, decolonial, and pluriversal AI design.

In this course we develop a grounded literacy of human and artificial intelligence, tracing AI’s historical, social, and political roots. Participants reframe intelligence as co-decision-making between humans, data, and institutions, exploring the concept of decision intelligence, and applying ecosystem thinking to critically navigate technological infrastructures, power, and bias, fostering responsible decision-making in complex contexts.

In this course we examine how corporations adopt, govern, and debate AI in practice. Through case studies, debates, and experiential activities, participants develop critical insights and practical strategies for responsible AI adoption in business contexts.

In this course we explore AI as an emerging economic force, examining narratives and movements shaping business, society, and climate futures. Using project-based methods, participants analyze trends, critique assumptions, and develop counter-scenarios. Guest sessions and hands-on exercises guide participants in translating AI narratives into responsible business cases with risk analysis and planetary KPIs.

In this course we critically examine Large Language Models and Natural Language Processing, exploring their technical, historical, and social dimensions. Participants learn to demystify AI systems, analyzing their infrastructure and social impacts, and situating technologies within broader cultural and historical contexts, developing a foundational practice in critical, socio-technical AI analysis.

In this track we explore ethical, human rights, and justice frameworks for evaluating AI’s impacts across individual, societal, and planetary scales. Participants critically examine social, economic, environmental, and more-than-human dimensions of AI impacts to identify inequities, and develop anticipatory, responsible and intersectional reasoning around current and emerging applications of AI.

In this track we examine social, cultural, and economic impacts of AI across topics such as labor, welfare, culture, and democracy through a guest lecture series with leading experts coming from academia, business, arts, policy and tech.

In this track we equip participants with critical and technical methods to assess and mitigate ethical, social, and environmental risks of AI. Participants design interventions and accountability tools to enhance transparency, fairness, and sustainability, applying anticipatory and systemic thinking to develop responsible innovation strategies for AI deployments.

In this course we explore what responsibility means in research and innovation around digital technologies. Participants reflect on and assess diverse socio-technical systems worldwide, applying frameworks for responsible design. Through discussion and analysis, they examine AI research and projects where responsibility is actively integrated as a guiding principle.

In this course we study global and European AI policy initiatives, including the EU AI Act and Trustworthy AI guidelines. Participants analyze regulatory frameworks, debates, and case studies, developing critical insight into how policies shape ethical AI adoption, corporate practices, and societal outcomes, preparing them to navigate and influence AI policy landscapes responsibly.

In this course we focus on the design and implementation of governance, compliance, and safety strategies for AI systems. Through workshops, case studies, and collaborative exercises, participants develop tools to ensure transparency, accountability, and ethical alignment, translating legal and ethical principles into actionable practices for responsible and safe AI deployment in public and private contexts.

In this course we introduce the philosophy and ethics of technology, exploring perspectives on technological neutrality, determinism, and normative design. Participants engage with ethical theories, from deontology to care ethics and Buen Vivir, and apply them through mediation analysis, impact assessments, and professional ethics exercises, developing skills to critically evaluate and responsibly enact technology in practice.

In this course we guide participants to explore personal situatedness, organizational roles, and design-oriented strategies for scaling ethical practices. Through reflection, role-play, and hands-on prototyping, participants identify blind spots, leverage strengths, and develop practical approaches to embed responsible AI practices and inclusive, impactful ethics in real-world contexts.

In this course we demystify AI definitions, frameworks, and practices, guiding participants through regulations, AI system lifecycles in corporate landscapes. Using exercises, case studies, and critical making, participants explore responsible AI, governance, evaluation, and sociotechnical imaginaries, developing practical skills to audit, design, and operationalize AI responsibly while navigating organizational dynamics.

In this course we equip participants with key skills to embed responsible practices into AI strategy and its implementation. Through case studies and hands-on exercises, participants learn to align AI deployment with ethical principles, build governance frameworks, manage risks, and integrate transparency, fairness, and accountability across the AI lifecycle.

In this course we explore how design can advance Trustworthy AI by translating values into technology and material culture. Participants use imaginative, prototype-based ethical thinking to resolve value conflicts, operationalize principles like fairness, privacy, and transparency, and develop actionable strategies for AI decision-makers and development teams, moving beyond risk mitigation toward proactive value-centered design.

In this track we explore media as a site for experimentation and critical inquiry of AI. Participants use iterative prototyping, speculative design, and creative coding to interrogate cultural, social, and technological systems, developing outputs that challenge their assumptions of Responsible AI.

In this track we co-create a distributed and open learning environment to process insights from other modules. Through group workshops, peer-to-peer mentorship, and cross-disciplinary projects, participants develop strategies for knowledge synthesis, collaborative storytelling, and reflective practice, fostering engagement with communities while connecting societal, ecological, and cultural contexts.

In this track we challenge how media and technology shape perceptions, values, and social futures. Participants critically analyze and prototype interventions that explore emerging socio-technical imaginaries, connecting creative practice to ethics, policy, and governance, while experimenting with formats that communicate insights to diverse audiences.

In this phase participants conduct research, explore contexts, and define project objectives, using iterative feedback to refine questions, frameworks, and methodologies that will guide the design and development of their final project.

In this phase, participants translate research into actionable proposals through design fictions, tools, narratives, and media experiments. Participants apply transdisciplinary methods and creative techniques to develop prototypes, iteratively testing ideas while integrating ethical principles throughout the project.

In this phase, we develop strategies to share projects with wider audiences. Participants craft compelling narratives, document processes, and present findings using diverse formats, from reports to video essays. Emphasis is placed on accessibility, public impact, and reflective dialogue, culminating in internal and public presentations with feedback from peers, tutors, and external experts.