23 augustus 2023
Understanding the responsibilities of developers in creating generative AI products. Analyzing the ethical implications of generative AI and its impact. Exploring strategies to ensure accountability and mitigate potential risks
HCAIM Consortium
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.
Understand the strengths of existing AI laws: Protection of Privacy Non-Discrimination Intellectual Property Liability Framework Understand the limitations of existing AI laws: Rapid Technological Advancement Complexity and Interpretation Transparency and Accountability Enforcement Challenges Bias and Discrimination Lack of Global Consensus Ethical Gaps Access to Justice Regulatory Capture
The objective of the course is to introduce the students to the legal environment of AI, including especially the basic principles and guidelines and the present and possible future framework of these laws, especially the Artificial Intelligence Act of the EU. The lecture helps recognise and mitigate the risks, know the accountability and governance implications of AI, and what we need to do to ensure lawfulness, fairness, and transparency in AI systems.
1 december 2021
The concept of professional responsibility is a component of ethics relevant in particular to how ethics relates to practical training. A sense of personal morality or the obligations one has to other roles can be distinguished from the responsibility someone has as a member of their profession. In this context we will focus on what the profession demands, independently from the degree to which all responsibilities may or may not be judged to align. This will allow a clearer view of exactly what it is that demanded by the workplace as opposed to other elements of one’s life.
Miranda Nell
19 maart 2026
Main Concepts Technical / engineering testing: Checking whether the system or VR build works correctly, meets requirements, and behaves consistently. Research / participant testing: Testing with participants to verify that the experiment supports the hypothesis and produces valid data. Debugging: Systematically identifying, isolating, and fixing the cause of a technical problem. Learning Goals Distinguish between the two types of testing. Apply a simple technical testing protocol to a project. Systematically identify, document, and fix software or VR errors. Use the Testing Worksheet to maintain traceability and collaboration. Recognize testing as essential quality assurance for both research and development. Key Takeaways Technical testing validates the build before research testing validates the hypothesis. Debugging is guided observation — not random trial and error. Documenting your tests ensures accountability and repeatability. Continuous testing builds confidence and prevents surprises during research trials. A clear testing workflow saves time and makes collaboration smoother. Further Exploration Search for “Unity debugging and testing workflow.” Search for “How to create software testing protocols.” Search for “Difference between pilot study and technical testing.” Search for “Best practice bug tracking templates.”
Lucia M Rabago Mayer, Jordi Weldink, Amanda Poulin
23 juli 2026
Kennismaking met UB en wetenschappelijke literatuur
Rian Kriesels, Kim Dibbets, Linda Schoonmade, Emma Besijn, Sebastien Valkenberg, Arjan De Rooy, Ruben Wiering, Michèle Meijer, Anouk Nuijten, Rick Vermunt, Marijn de Groote, Harm le Grand (Kennisnet), Linde Voorend
11 september 2025
28 augustus 2025
3 december 2018
Manual AnatomyTOOL how to create an account
Auteur onbekend