Master's Degree (MSc) in Responsible Artificial Intelligence
- Study level Postgraduate
- Credits 90 / 120 ECTS - EQF > / MQF > Level 7
- Duration 3 or 4 terms (12-24 months)
- Location Online / Fully remote
- Next intake September 2025
- Language English
- Tuition Fee €6750
- Accreditation Accredited >
Overview
The MSc in Responsible AI combines technical expertise with a focus on the ethical implications of modern AI, covering foundational and advanced concepts without extensive mathematical detail.
It emphasizes real-world applications in areas like natural language processing, computer vision, business, sensor systems, and industry automation. The program provides an overview of advanced computing architectures for sustainable AI systems, emphasizing environmental impact. Ethical considerations and social responsibilities are integral to every application discussed.
The program provides flexibility with two exit paths, as students can finish the Capstone Project in one or two terms.
It emphasizes real-world applications in areas like natural language processing, computer vision, business, sensor systems, and industry automation. The program provides an overview of advanced computing architectures for sustainable AI systems, emphasizing environmental impact. Ethical considerations and social responsibilities are integral to every application discussed.
The program provides flexibility with two exit paths, as students can finish the Capstone Project in one or two terms.
EU accredited Institution
Tutors available on a daily basis
Recorded & live content
Strong exposure to real-life business application
Progressive assessments (no final exams)
Flexible pace, leading to a completion in 12-24 months
Career Outlook
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Graduates of this MSc program will emerge as versatile AI professionals, adept in foundational and advanced AI concepts. They will apply AI principles responsibly to develop applications, models, and software, addressing real-world challenges in industry and research. The program unlocks diverse career opportunities, with graduates well-prepared for roles such as:
- AI Application Developer
- AI Researcher
- AI Product Manager / Designer
- Data Scientist / Analyst with AI Focus
- Machine Learning Engineer
- AI Business Strategist / Consultant
- AI Entrepreneur / Startup Founder
- Computer Vision Engineerd
Top-notch academic leaders
Our selection process focused on recruiting Professors with great academic experience, a strong knowledge of today's Computer Science, and a hands-on teaching approach.
At OPIT, we empower future leaders to apply modern AI responsibly and effectively, bridging technology, ethics, and real-world applications.
Pierluigi Casale
Area Chair in Artificial Intelligence @ OPIT, Former Innovation Officer @ European Parliament
Zorina Alliata
Principal AI and Generative AI Strategist @ Amazon, Chair of the Artificial Intelligence Committee @ AnitaB.org
Germany
Moez Ali
Founder and Creator @ PyCaret, Product Director - AI @ antuit.ai
Canada
Jonna Koivisto
Former Research and Development Lead @ Solsten, PhD on Information Studies and Interactive Media @ University of Tampere
Germany
Charafeddine Mouzouni
Founder @ Cohorte, Head of AI Consultant @ LEX AI
France
Syllabus
The program includes two terms of taught courses (60 ECTS) and one or two final terms for the thesis or project. Students can choose to complete the Capstone Project in one term (90 ECTS) or extend the MSc thesis project over two terms (120 ECTS).
The final project can be structured as an internship in a company or research lab, with facilitated opportunities available.
Each term spans a duration of 13 weeks.
- Fall term from mid-late September to mid December
- Spring term from late January to late April
- Final term (Capstone): mid-May to completion
First Term 
Introduction to AI and Ethics
This course explores the transformative impact of Artificial Intelligence (AI) on our world. It covers AI's history, algorithms, and ethical considerations, emphasizing societal impacts and ethical frameworks guiding development, deployment, and regulation. Students gain critical thinking skills to address ethical issues in AI, including fairness, accountability, transparency, algorithmic bias, and decision-making explainability. The module also explores data privacy laws, including the AI Act.
Data Analytics and Visualization
This course addresses the challenges of handling complex and large datasets in modern AI applications. The module, conducted in Python, covers data preprocessing, cleaning, storytelling, and communication. It explores techniques such as linear and nonlinear dimensionality reduction, manifold learning, and visualization of graph-structured data. Students gain essential skills to conduct exploratory analyses and design effective AI pipelines for intricate datasets.
Human-Centered AI Design
This course emphasizes prioritizing human experiences in the era of Artificial Intelligence (AI). The module equips students with skills to develop AI systems that respect human values, needs, and inclusivity. Topics cover user experience design methods, human-computer interaction, ethical AI design principles, and analysis of real-world examples. Students gain insights to design AI interactions that enhance human capabilities for societal benefit.
Programming for AI
In the AI era, programming is crucial in unlocking the potential of intelligent systems. This module covers essential programming tools for modern AI systems using Python. Topics include Python fundamentals, Jupyter Notebooks, advanced data structures, object-oriented and functional programming, numpy, pandas, scikit-learn, and version control systems like GitHub. Students gain skills in programming concepts, data visualization, and tools necessary for AI development.
Machine Learning
The course in Machine Learning covers the foundational concepts and techniques of building and training intelligent computer systems. This includes topics such as probability and statistics, supervised and unsupervised learning algorithms, deep learning, natural language processing, computer vision, and reinforcement learning. Students also learn about the ethical and societal implications of machine learning, as well as practical applications in various industries such as finance, healthcare, and marketing. The course involves both theoretical lectures and hands-on projects that require programming and data analysis skills.
Second Term
Natural Language Processing
In a data-driven world, mastering human language processing is crucial. This course equips students with fundamental knowledge and practical skills for working with natural language data. Topics include text mining, sentiment analysis, language generation, machine translation, symbolic methods for NLP, deep learning models for NLP, and ethical considerations in AI-based NLP. Students also explore specialized libraries and tools for Natural Language Processing based on modern deep learning methods.
Computer Vision
The elective course in "Computer Vision" covers the fundamental principles and techniques for interpreting and analyzing digital images and videos using computers. This includes topics such as image processing, feature extraction, object recognition, machine learning, deep learning, and 3D vision. Students will learn how to develop computer algorithms to perform tasks such as image classification, object detection, and tracking. Applications of computer vision can be found in a wide range of fields including robotics, medical imaging, autonomous vehicles, and security.
Computing Architectures for AI
In the evolving AI landscape, this course explores various computing architectures shaping AI innovation. It covers clusters, cloud systems, GPUs, TPUs, neuromorphic hardware, photonic computing, and quantum computers. Emphasizing pros and cons, the module evaluates sustainability, considering power consumption, CO2 production, and environmental impact.
AI for IoT and Automation
This course explores the transformative synergy of Artificial Intelligence (AI), the Internet of Things (IoT), and automation. Students gain insights into AI's significance in IoT, types of sensors, edge computing, and integrating AI for automation. The module covers computer vision applications in IoT, video analytics, and real-world cases in smart cities, healthcare, and agriculture. It also addresses future technologies like Edge AI, 5G, and AIoT, considering ethical, privacy, and regulatory aspects.
AI in Business, Strategy, and Entrepreneurship
In the digital transformation era, businesses and entrepreneurs explore unique opportunities with the integration of artificial intelligence (AI). This course explores AI's role in diverse business sectors, emphasizing strategy, leadership, innovation, and applications in supply chain optimization, financial analysis, marketing, and customer engagement. The module delves into entrepreneurship and AI startups, addressing legal and ethical implications through case studies and industry insights, preparing students for AI-driven business landscapes.
Research Methods and Tools
The course covers various methods and tools that are essential for conducting research in different academic fields. The course is designed to help students understand the research process and develop the necessary skills and knowledge to conduct high-quality research. Topics include Research Design, Data Collection Methods, Data Analysis, Research Writing and Presentation, Literature Review, and Research Ethics.
Third Term
Capstone Project and Dissertation
The Capstone Project and Dissertation, also known as the MSc thesis, is the pivotal project in the MSc program, aimed at consolidating acquired skills through a research endeavor. Collaborating with an OPIT supervisor, each student develops a project proposal realized in the final terms of their MSc program. The project entails research of industrial relevance, exploring methodological and/or practical aspects within program domains and beyond. Depending on the chosen exit point (30 or 60 ECTS), the MSc thesis workload is distributed over 1 or 2 terms, respectively. Students also have the opportunity to integrate internships with industrial partners to complement and complete their MSc thesis.
Entry requirements
Candidates for this MSc degree program should possess prior technical expertise. Applicants lacking the necessary technical background will need to undergo an assessment to assess their foundational IT and competencies.
BSc degree in STEM fields or other disciplines
English proficiency as B2-level certificate or equivalent
If you don't have an English certificate, try our test
Value for money
Thanks to several funding options, our fees start at an affordable rate per term. Plus, online learning will save you money as you won't need to relocate to pursue your degree.
Fully inclusive
Our course fees are all inclusive, with no hidden charges.
Scholarships & Funding
We have different financing options to fund your degree.
Master Fees
€6750
€2250/term
-
MSc program of 120 credits: €9000 -
Different payment plans available
-
10% discount if you pay upfront
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