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HBO/WO

MBO

September 1, 2025

Economics in the Digital Age: Insights and Applications

This book serves as an essential resource for both undergraduate and graduate programs due to its comprehensive approach to digital economics: Theoretical Integration: It integrates classical economic theories with the digital economy, demonstrating how traditional principles apply to new digital realities. Practical Frameworks: It provides actionable insights for analyzing digital market trends and monetization strategies, relevant for students, managers, and business professionals alike. Case Studies and Real-World Applications offer concrete examples of digital transformations, making complex concepts more accessible and useful for research and coursework. Advanced Analytical Tools: The book helps students develop the analytical skills needed to evaluate digital business strategies, market dynamics, and the broader economic impact of digital technologies. For professionals, this book provides strategic guidance on navigating the challenges and opportunities of the digital economy. By understanding the core economic principles behind digital business models, managers can make informed decisions on everything from digital marketing to product development, all while anticipating and mitigating risks like cybersecurity threats or regulatory changes.

Open (Access) Text Book

Shilpa Samplonius-Raut

August 23, 2023

Interactive Session: Privacy Preserving Machine Learning

Understand the concept of Privacy Preserving Fundamentals Discuss application of PPML in practical Scenarios

Website

HCAIM Consortium

August 23, 2023

Lecture: Data Security and compliance, Data lineage and Management

Understand the concept of data security, compliance and lineage Understanding of related techniques and approaches

Website

HCAIM Consortium

August 23, 2023

Lecture: Introduction to privacy and risk

Obtain a general understanding of the notion of privacy. Understand the difficulties and pitfalls of data privacy analysis and detection of personal data. Understand the trade-off between anonymization and data utility (no free lunch). Understand the difference between data security and data privacy. Learn the basic principles of anonymous communication and TOR. Discern, investigate and discuss key risks which AI and Machine Learning models introduce

Website

HCAIM Consortium

August 23, 2023

Lecture: Consumer Issues - Filter Bubbles, Data Storage, AI monitoring, fair practices

Identify and define key consumer issues Analyse the implications of filter bubbles and AI monitoring on consumer issues. Explore the fair practices used when dealing with AI consumers

Website

HCAIM Consortium

August 23, 2023

Lecture: Fair Operating Practices - AI Recruitment and Malpractices of AI Monitoring

Understand the principles of fair operating practices in AI recruitment, including the importance of transparency, accountability, and bias mitigation. Identify responsible AI monitoring practices, including informed consent and transparent policies. Recognize potential ethical concerns and legal frameworks for using AI in recruitment and monitoring.

Website

HCAIM Consortium

August 23, 2023

Lecture: Governance and Stewardship, Key Stakeholders and Personal data Management

Understand the concept of Governance and Stewardship, Key Stakeholders and Personal data Management Understanding of related techniques and approaches

Website

HCAIM Consortium

August 23, 2023

Interactive session: Risk & Risk mitigation

Recognize, analyze and discuss the necessity and the role of security testing in AI. Employ documents, such as the AI Act, to classify use cases into risk categories. Discuss in-depth and with common European vocabulary on AI risk and how can AI be designed safely and benevolently, in ways that will not expose humans to unacceptable risks of harm, even when risky behaviour may advance a system goal. Classify the obligations of providers and users in relation to AI risk management. Discuss the requirements for high-risk AI systems as per the AI Act. Elaborate and recognize specific risks and risk taxonomies, relevant throughout the different stages of the AI lifecycle.

Website

HCAIM Consortium

May 31, 2023

Data Privacy Handbook

The Data Privacy Handbook is a practical guide on handling personal data in scientific research, primarily written for Utrecht University researchers and research support staff in the Netherlands. It is an initiative of Research Data Management Support, in collaboration with privacy and data experts at Utrecht University. The Data Privacy Handbook consists of: A knowledge base which explains how the EU General Data Protection Regulation (GDPR, Dutch: Algemene Verordening Gegevensbescherming) applies to scientific research, including guidelines and good practices in carrying out GDPR-compliant scientific research; An overview of privacy-enhancing techniques & tools and practical guidance on their implementation; Use Cases in the form of research projects with privacy-related issues, for which a reusable solution (e.g., tool, workflow) is shared.

Website

Universiteitsbibliotheek Utrecht

May 19, 2023

Privacy Basics for Researchers: online module materials

These are the raw materials used in the online training module "Privacy Basics for Researchers", created within Utrecht University's Moodle platform, by Utrecht University's Research Data Management Support. This module was developed as part of the Data Privacy Project, to give Utrecht University researchers a basic understanding of the General Data Protection Regulation in a research context. It is a self-paced module that takes about 1.5 hours to complete. The module focuses on the following topics: Chapter 1: Personal data, sensitive personal data, roles in the General Data Protection Regulation (GDPR) Chapter 2: Legal basis in research, informing data subjects, data subjects' rights Chapter 3: Privacy by Design and Privacy by Default, privacy scan, Data Protection Impact Assessment (DPIA), privacy risks, data breach Chapter 4: Data security, access control, encryption, pseudonymisation, anonymisation, tools Chapter 5: Storing and sharing personal data, making personal data Findable, Accessible, Interoperable and Reusable (FAIR) In the deposit, you can find the following files: An overview of the module's blocks and learning goals (Learning-Goals_Privacy-Basics-for-Researchers.pdf) The module content in the form of a storyboard per chapter in the online platform Self-created images used in the platform (Platform_materials.zip). A raw Moodle export of the module that can be imported in any other Moodle platform (Privacy_Basics_for_Researchers_pbfr_20230512.mbz)

Document

Dorien Huijser, Ruud Dielen, Jacques Flores-Dourojeanni, Mercedes Beltrán