Human Centred Artificial Intelligence Master (HCAIM)

This module establishes a theoretical foundation in AI, Machine Learning, and Data Exploration, extending it with Decision Theory. The curriculum supports rigorous conceptual understanding with preparation for practical applications, ensuring students are well-equipped to address AI ethical challenges.

This module aims to develop the learners' understanding of rationality, approaches to inductive inference, and the importance of understanding the data. This module conveys a modern view of fundamental challenges and approaches in AI, machine learning, and data analysis. It focuses on the first phase of the MLOps lifecycle and is related to the lowest maturity level of the application of Machine Learning (ML) in organizations: modelling data. It includes the activities that form the basis of the application of ML, such as data extraction, data analysis, data preparation, model training, and (mainly manual) model validation and evaluation.

This module employs a combination of lectures and interactive demos to support understanding. The topics include General AI, Data Exploration for Machine Learning, Machine Learning Fundamentals, Decision Theory, Foundations of AI, Human decision making, White-box decision support models.

In this module, the focus is on correctly analyzing and modelling the data to achieve the business objectives and little use is made of automation (e.g. CI/CD), which is only added in the second phase of MLOps (Deployment – Module B). The modelling activities are often characterized by the manual, script-driven and interactive method by which the data analysis, preparation, model training and validation are carried out. To maintain an overview of the different models, parameters and choices that are being experimented with, experiment tracking is used.

This practical-oriented module on Data Science, Supervised and Unsupervised Machine Learning, and their applications. Students gain hands-on experience with both software and hardware platforms, enhancing their technical skills in implementing AI solutions. The coursework emphasizes deploying machine learning models across various real-world scenarios. Alongside technical training, the module prepares students to consider the societal impacts of their work and to develop AI solutions that are both innovative and responsible.

This module emphasizes the ethical considerations crucial in AI development, particularly during the modelling phase. Students learn to rigorously define clients' objectives, identify and understand stakeholder values, and navigate potential conflicts among them. The curriculum prioritizes transparency, inclusion, security, and privacy, ensuring these principles are integral to AI solutions. Moreover, the course stresses the importance of scrutinizing the social and moral desirability of client objectives. Students are trained to recognize biases in data, understand their potential impacts, and develop strategies to mitigate these biases, thus fostering responsible and equitable AI applications.

The module includes the following topics: General Ethics, Ethical Frameworks, Advanced Ethics, Applied Ethics, Moral deliberation, Fundamental human rights.

This module aims to develop the learners' understanding and appreciation of emerging and future Artificial Intelligence and Machine Learning technologies. Learners will be required to investigate novel applications, research areas, and environments where these technologies can be beneficial. This module considers the most current, up-to-date, and emerging research areas in Artificial Intelligence and Machine Learning (including aspects of broader technology). Naturally, these fields will change rapidly and must be researched regularly.

This module employs a combination of lectures and interactive sessions to discuss open problems and challenges surrounding future AI. Topics include Explainable AI (XAI), privacy concerns, normativity, model drift, and Artificial General Intelligence (AGI). Additionally, it explores advancements in ML models, such as Generative Models, Federated Learning, and Model Compression, complemented by tutorials and lab sessions. Finally, the module concludes with philosophical discussions on the Permeation of AI and AI Singularity, living alongside robots, and Quantum Computing.

This module first discusses AI applications' positive and negative externalities. It delves into Corporate Social Responsibility (ISO 26000) within the context of employing Human-Centric AI (HCAI) systems, emphasizing fair operating practices in AI recruitment and monitoring. Interactive sessions explore AI-based decision-making in recruitment, promotion, and monitoring, alongside human intervention in AI-driven decisions and the transfer of control between humans and AI agents. Psychological aspects of working with AI, such as stress and anxiety, are also discussed. Lectures and interactive sessions delve into consumer issues like data storage, AI monitoring, and fair practices, focusing on societal impact assessment and community development. Socio-legal aspects of AI responsibility and product copyright issues are examined, along with discussions on addressing economic gaps and the digital divide.

This module finally explores how AI influences human behaviour, environmental impact, education, filter bubbles, and AI-powered warfare, fostering a comprehensive understanding of AI's societal implications.

This module examines EU and international legislation and frameworks concerning data, AI, human rights, and equality. It encompasses an overview of ethical, professional, and legal aspects pertinent to Human-Centric AI (HCAI) applications. Interactive sessions offer deeper insights into the ethical, professional, and legal dimensions of HCAI applications. Lectures shed light on the challenges posed by data regulations such as EU GDPR, US COPPA, and HIPPA, data sourcing and the prospective impact on HCAI. Discussions extend to EU Human Rights Legislation, complemented by case studies to illustrate practical implications. Moreover, lectures and interactive sessions scrutinize the EU Regulations on HCAI applications, evaluating its efficacy through practical assessments.

The module further delves into the strengths and limitations of existing laws, focusing on data management, audit, assessment, security, compliance, governance, stewardship, and key stakeholders. Practical sessions explore the roles and cross-overs between data management and AI teams and investigations into data lineage challenges and potential impacts on AI teams.

The Graduation module reflects the core principle of the HCAIM programme that is built on the concept of project-based learning (PBL). The goal of this module is to position the graduation project (making a professional product) centrally in the student’s learning trajectory. As part of their Graduation project (the Master Thesis), students show that they can independently solve challenges proposed by the industry based on current needs and requirements, considering both the technical and the ethical aspects of the issue at hand.

Each thesis is considered locally, with an internal supervisor (a professor from the University in which the student is pursuing the degree) and an external supervisor belonging to the party proposing the thesis (if any). This latter aspect, despite not being mandatory, is rigorously pursued. The proposing party can be an SME, an Excellence Centre, or another University, both at a national and international level. Proposing parties are expected to provide both national and international thesis (i.e. thesis organised in with a University from the same country or from a foreign one).