A two-day masterclass for practitioners at INVI—the Institute for wicked —that provides you with a roadmap to follow when using AI in your organization.
AI systems are rapidly moving from pilot projects to becoming a central part of organizations' infrastructure. They shape decisions, workflows, services, and strategic priorities—often in ways that can be difficult to fully predict. Ethical issues are therefore no longer confined to conference rooms or academic debates. They arise in communication strategies, procurement processes, data flows, supplier collaborations, and implementation choices. Many professionals today bear real responsibility for these decisions but lack a clear framework for navigating the complexity. Often, it is not uncertainty about the potential of AI that slows organizations down, but uncertainty about how to act more responsibly while realizing that potential. Where do you start? How do you translate AI ethics into the organization—not just as a list of rules, but as a practice that connects innovation with the organization's core values?
AI Ethics in Practice is a masterclass for professionals grappling with precisely these questions. The course is held at INVI—the Institute for wicked —and draws on tools developed with support from the Danish Data Science Academy (DDSA). The course offers structured tools, shared reflection, and application-oriented frameworks that go beyond a narrow focus on GDPR and data protection. Participants work with concrete cases from their own organizational contexts. The goal is to equip you to translate ethical ambitions into concrete action, and thus the masterclass frames AI ethics as practice: A space where principles are embedded in decisions, and theory becomes lived organizational practice.
The course is taught by Sofie Burgos-Thorsen (Ph.D., Head of Research and Analysis at INVI) and Tess Skadegård Thorsen (Ph.D., expert in tech and AI ethics). Two internationally recognized researchers and practitioners who combine academic expertise with hands-on experience in AI innovation, implementation, and evaluation. Drawing on Design Justice, Data Feminism, and critical data studies, they translate research-based perspectives into concrete tools that you can take directly back to your organization.