The 'Algorithmic Bias' EQF6 course competence matrix serves as a foundational element for two other critical components: the 'Ethical AI Micro Credential' EQF4 competence matrix and the 'Learning Outcomes for Adults and Youth EQF2 (Serious Game)' learning outcomes. These three components are interconnected and designed to complement and reinforce each other, ensuring a comprehensive understanding and application of ethical AI principles across various educational levels and contexts.
The 'Algorithmic Bias' course competence matrix lays the groundwork for the development of the 'Ethical AI Micro Credential' EQF4 competence matrix. By providing a strong foundation in understanding algorithmic biases and their implications, this course empowers learners to delve deeper into the ethical dimensions of AI as they progress to the EQF4 level. The 'Ethical AI Micro Credential' EQF4 competence matrix builds upon the initial competencies, expanding the scope to include broader ethical considerations and practical applications of ethical AI principles in various sectors and scenarios.
Simultaneously, the 'Algorithmic Bias' course competence matrix also informs the 'Learning Outcomes for Adults and Youth EQF2 (Serious Game)' learning outcomes. This connection ensures that the fundamental concepts and skills developed in the course are accessible and engaging for learners at the EQF2 level. The serious game format is designed to introduce these critical topics in a manner that is both entertaining and educational, fostering a deeper understanding of algorithmic bias and ethical AI principles among a wider audience.
External Validation Report Podcast of the Competency Matrices for Algorithmic Bias This resource has been developed using Google's virtual research assistant NotebookLM.
This course focuses on understanding and addressing algorithmic biases, equipping professionals, academics, and students with knowledge and practical skills to identify, mitigate, and manage biases in AI systems. It emphasizes the ethical, social, and technical implications of algorithmic biases across sectors like healthcare, education, finance, and government. By completing this program, participants will gain a comprehensive understanding of algorithmic biases, their societal impact, and effective strategies to address them, fostering more equitable and ethical use of algorithms in decision-making processes.
This educational program is designed for professionals, academics and students, equiping participants with the knowledge and skills to address biases ethically and responsibly and explores the foundational concepts of algorithmic bias, including its causes and manifestations while emphasizing essential ethical principles such as non-maleficence, fairness, transparency, accountability, and respect for human rights. Participants will delve into practical approaches for minimizing harm, enhancing fairness, ensuring transparency, and promoting accountability in AI systems. The program also investigates the role of legal frameworks, stakeholder collaboration, and interdisciplinary approaches in ethical AI development.
Through interactive activities and scenarios, Charlie's House fosters critical thinking and problem-solving skills, enabling learners to identify biases in AI applications and explore ethical principles like fairness, transparency, and accountability. Participants engage with real-life examples, collaborate to address bias-related challenges, and reflect on their own perspectives and responsibilities in AI use. Upon completion, learners will have a foundational understanding of algorithmic bias, improved teamwork and communication skills, and a heightened sense of ethical awareness and civic responsibility in advocating for fair and accountable AI systems.
To ensure long-term impact, the CHARLIE project has developed a dedicated Exploitation and Sustainability Plan, outlining how the project’s key educational resources will remain accessible, integrated, and relevant after the project ends.
This document includes strategies for formal adoption by educational institutions, recognition of the microcredential, and actions by consortium members to promote continued use across Europe.
On this page, take a look at the materials developed
ADMINISTRATORS' GUIDELINE
This guideline is designed to support academic administrators, curriculum developers, and faculty staff in the successful implementation and adaptation of the CHARLIE EQF Level 6 course “Algorithmic Bias: Ethics and Fairness in AI Systems. It provides clear instructions on how to integrate the course into existing university programmes (Bachelor's or Master's), how to assess learners, how to adapt the modules to national or institutional needs, and how to align with quality assurance frameworks. The document also includes recommendations for credit allocation, cross-disciplinary collaboration (e.g. with Law or Ethics departments), and suggestions for evaluation procedures and student feedback loops.
ADMINISTRATORS' GUIDELINEInteractive eLearning Screens
(include incorporated expert videos and booklets at the end of each unit)
Formative assessment exercises
Summative assessment tests
Interactive concept maps
Roulette Game
Interactive Flipbooks
Support PowerPoint presentations
Handbook
Multiple choice quizzes
Open answer formative assessment
Expert videos
Case study
Project-based exercises
On this page, take a look at the materials developed
Policy Recommendations for Integrating the 'Ethical AI' Microcredential into Higher Education Admission Pathways
This document presents a comprehensive set of policy recommendations developed within the framework of the CHARLIE project to support the formal recognition and integration of the “Ethical AI” microcredential (EQF Level 4) into national higher education admission pathways across Europe. Building on EU-level frameworks—such as the European Qualifications Framework, the Digital Education Action Plan, and the Council Recommendation on Microcredentials—it outlines practical strategies for ministries, accreditation agencies, and universities to adopt and promote this microcredential as a valid learning pathway for access to tertiary education. The recommendations are supported by national policy analysis from five partner countries (Portugal, Spain, Finland, Denmark, and Romania) and highlight how the “Ethical AI” microcredential meets urgent labour market needs, ethical digital skills gaps, and the broader goals of a human-centred, inclusive digital transition.
POLICY RECOMMENDATIONEthical AI micro credential course - Challenging Bias in Big Data user for AI and Machine Learning
Interactive eLearning Screens
(include incorporated summative quizzes and booklets in each unit)
Policy recommendation for recognition of microcredential in HE
Podcast Understanding Algorithmic Bias in AI Systems (WP4 CU Booklet podcast)
