Lesmateriaal voor een werk/hoorcollege om studenten de basis van AI-literacy bij te brengen. Dit lesmateriaal bevat een PowerPoint en een lesplan en is gemaakt voor studenten Medische Informatiekunde, faculteit Geneeskunde UvA/ Amsterdam UMC.

Het doel van dit implementatieplan is om alle studenten van een opleiding toegang te geven tot een generatieve AI toepassing. Om de implementatie van toepassing succesvol te laten verlopen, hebben we in deze OER een handleiding, een implementatieplan, een communicatiebericht en links naar trainingen (beschikbaar in andere OERs van dezelfde instelling) opgenomen.

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.

Deze e-module biedt basiskennis van AI. Het doel is dat je begrijpt wat AI is en hoe je het op een verantwoorde manier kunt inzetten. Het is belangrijk om je bewust te zijn van jouw verantwoordelijkheid als het gaat om het gebruik van AI.

Join Luiz Canalle as he explores the history of Artificial Intelligence (AI), from early game-playing programs in the 1950s to today's advanced systems. Learn about the rise of recommendation algorithms, and the advent of Generative AI. Understand the difference between Narrow AI and General AI, and delve into the principles of machine learning and deep learning. Stay informed and critical about AI's impact on our lives. #humaninthelead

This module is intended for all staff and students at HAN. It is also available to interested parties outside HAN. There is also a Dutch version available called: Kennismaken met AI in de HAN.

In this module you will learn to understand what Generative AI is, recognise the opportunities and limitations of Generative AI, and familiarise yourself with the do’s and don’ts when using Generative AI.

This lecture considers the relevance of the cognitive sciences for law when it comes to acts and responsibility (or liability) of artificially intelligent agents. It addresses whether we (1) can, and (2) should hold AI liable. The lecture is part of project RECOGNISE. RECOGNISE aims at developing an interdisciplinary training curriculum on legal reasoning and cognitive science, filling a gap in legal higher education. It is a strategic partnership of six European universities for higher education sponsored by the ERASMUS+ Programme. The project will provide the interested learners (legal researchers, law students, legal practitioners, and beyond) with introductory and advanced materials on topics including heuristics and biases in adjudication, cognitive structure of legal concepts, and defeasible reasoning in law. Materials will be provided in the form of an online handbook, video lectures, summer schools, and other online and on-campus activities.

- Understand, analyze and elaborate upon the importance of XAI in the modern world. - Differentiate between transparent and opaque machine learning models. - Categorize and discuss approaches to explainability XAI based on model scope, agnosticity, data types and explanation techniques. - Discern, investigate and discuss the trade-off between accuracy and interpretability. - Summarize and understand the working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanations. - Expand on possible applications of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.

Studenten maken nog maar weinig gebruik van AI of slechts op een ineffectieve manier. Deze site is gemaakt om studenten te helpen om de kracht van AI te gebruiken voor meer studiesucces in minder tijd.