Responsible AI Leadership
In the last few years, we have seen widespread adoption of Artificial intelligence (AI). In this course, participants will explore the ethical, normative, and societal implications of AI and mechanisms to ensure that AI systems remain accountable and advance the social good.
About the course
AI technologies increasingly mediate our interaction with organizations both private and public, through for example newsfeeds, recommendations, diagnostics, and analytics. With the increased adoption comes also risks associated with lack of transparency, discrimination, manipulation, and dangers to the democratic processes. Thus, increasingly, companies and public agencies must navigate evolving accountability demands and be aware of developing regulation surrounding the use of AI and related technologies.
In this course, combining theoretical foundations from data ethics, law and governance, and real-world inquiry, participants will build their ethical imaginations and skills for responsible use of AI.
Practically, using case studies, participants will explore current applications of quantitative reasoning in organizations, algorithmic transparency, and unintended automation of discrimination via data that contains biases rooted in race, gender, class, and other characteristics. The cases will be considered in the light of existing and proposed regulations in the European Union (EU) such as the General Data Protection Regulation (GDPR), the proposed AI Act and proposed Data Governance Regulation.
The course will also introduce students to the global regulatory landscape, and ongoing efforts to make AI more accountable and work towards sustainable implementations of it.
Who is the course for?
Three main target groups:
- The Nature of Intelligence
- The History and Core Concepts of Artificial Intelligence
- Recent Developments and Impact of Artificial Intelligence
- The AI Life Cycle
- The Nature and Pitfalls of Data
- Navigating the Realites and Tradeoffs in Data Science
- Al Accountability, Transparency and Explainability
- AI Risk Management
- AI and Global Perspectives
- AI and Public Service Perspectives
- Governance frameworks for AI Implementations
- Risks and Benefits of Regulation
- Regulations governing AI
- Regulation through AI
- Upcoming regulatory developments
- Participation in Regulation – AI Sandboxes
- Open-Sourcing and Communities of Practice
- AI Impact AssessmentCo-Design and Stakeholder Engagement with and around AI
- Tackling Grand Challenges with AI
Samson Yoseph Esayas
The program is implemented with 4 sessions over a semester, with a total of approx. 75 hours.
The supervision offer will be somewhat different in the various Executive Master of Management Programs. Personal guidance and guidance will be given during the lecture. In general, students can expect advisory guidance, not evaluative guidance. The guidance offer is estimated at 2 hours per. task.
Teaching language is English.
Read the admission requirements for this programme.
The students are evaluated through a project assignment that counts for 60% of the total grade and an individual 72-hour home exam that counts for 40%. The project assignment can be written individually or in groups of up to three people. All exams must be passed for diplomas in the program to be awarded.
Module 1: 03.10 - 04.10
Module 2: 02.11 - 03.11