Course Description
This course critically examines normative, philosophical, and socio-political questions arising from the intersection of art, emerging technologies, and innovation systems. It addresses ethical challenges related to algorithmic bias, surveillance capitalism, automation, sustainability, authorship, and digital labor. Students engage with ethical theory and applied case studies to develop structured reasoning frameworks applicable to innovation governance and creative practice.
The course also provides a comprehensive examination of data governance frameworks, regulatory compliance, and lifecycle management in digital innovation environments (GDPR). It addresses the ethical and legal obligations associated with data collection, processing, storage, sharing, and deletion. Particular attention is given to European data protection regulation and its implications for product design, platform governance, and organizational accountability.
Learning Outcomes
Upon successful completion of this course, students will be able to:
Knowledge
- Demonstrate understanding of ethical theories relevant to technological and artistic practice
- Analyze socio-political implications of digital infrastructures and AI systems
- Demonstrate understanding of data governance models and lifecycle management frameworks
- Explain regulatory requirements associated with data protection and privacy
Skills
- Conduct ethical impact analyses of technological and creative projects
- Develop governance and mitigation strategies for identified ethical risks
- Design compliance-oriented data management processes
- Conduct privacy impact assessments and risk analyses
- Implement privacy-by-design principles in product development
Competencies
- Apply structured ethical reasoning to complex decision-making scenarios
- Advocate for responsible and socially accountable innovation
- Integrate ethical reflection into strategic and operational planning
- Ensure lawful and ethical data practices within innovation projects
- Balance innovation objectives with regulatory compliance
- Evaluate organizational readiness for data governance challenges
Key Topics Covered
- Normative ethical theories
- Algorithmic bias and fairness
- Surveillance and digital power structures
- Environmental sustainability in technological systems
- Authorship, labor, and creative ownership in digital contexts
- Data lifecycle stages and governance structures
- Privacy-by-design and data minimization
- Regulatory compliance frameworks
- Risk assessment and audit mechanisms
- Data security and breach response strategies