Ethics & AI + HPC Management

Students will be able to review an AI project for bias and ethical risks.

Students will be able to estimate the full lifecycle sustainability (manufacturing + training + use) of AI solutions.

Students will be able to formulate arguments regarding professional accountability.

Learning objectives:

• Analyze the ethical theories shaping the future of AI development and deployment.
• Utilize tools to assess social risks and legal impacts (including the EU AI Act).
• Understand the principles of designing fair and transparent algorithms.
• Review the full environmental lifecycle of AI: from hardware manufacturing and training to the massive energy costs of the use phase.
• Define professional responsibility and the necessity of human oversight in automated systems.
• HPC Management (BSC expert)

Unit 1: Foundations of Ethics in AI. [On-site Kick-off Session] Introduction to ethical definitions and the impact of technology on decision-making.

Unit 2: Ethical Evaluation & The Law. Overview of the EU AI Act, high-risk system classification, and compliance requirements.

Unit 3: Algorithmic Fairness. Methodologies for detecting bias in data and algorithms, and strategies for mitigation.

Unit 4: Professional Responsibility. Examination of liability, accountability frameworks, and the role of human oversight.

Unit 5: Green AI: Hardware, Training & Use. The full lifecycle: Manufacturing impacts, training energy, and the cumulative cost of the Use Phase (Inference).

Unit 6: HPC Management. Introduction to High-Performance Computing infrastructure at BSC.

Capstone Project (Weeks 11–12). Integration of concepts to analyze a complex, real-world ethical challenge. [On-site Final Presentation]

Eva Vidal

Dr. Eva Vidal is an Associate Professor at UPC, specializing in Ethics and Sustainability in AI and ICT. She teaches at ETSETB, FIB, and the Institute for Sustainability (IS.UPC). A member of the EduSTEAM research group, she actively collaborates with NGOs on ICT-based health and education projects. Dr. Vidal has held leadership roles as Director of the Center for Development Cooperation and Rector’s Delegate for Social Responsibility. She is a recipient of the Alan Turing Award for Social Commitment (2018) the UPC Social Council Award for Quality in University Teaching, and the Jaume Vicens Vives Distinction for Teaching Quality (2025).

This course serves as a comprehensive guide to responsible technology. It focuses on three critical pillars: Ethics (Fairness, Responsibility, Legal compliance), Sustainability (Material Lifecycle, Use Phase), and HPC Management.

The curriculum moves from theoretical frameworks to practical application. Students will engage with real-world problems, learning to identify bias in datasets, apply measures of AI fairness, and estimate the environmental cost of a deployed model. The goal is to prepare a generation of professionals who can ensure that AI systems are not only efficient but also legally compliant, environmentally sustainable, and socially responsible

<p>Open to students from diverse academic backgrounds. No advanced technical skills are required.</p>
<p>Motivation to explore the social and material implications of technology.</p>

<p>Certificate of achievement</p>

<p>Anatomy of an AI system <a href="https://anatomyof.ai/"><u>https://anatomyof.ai/</u></a></p>
<p><a href="https://ict-ethics-upc.blogspot.com/"><u>https://ict-ethics-upc.blogspot.com/</u></a></p>