Towards Human-Centered, Inclusive, and Equitable Artifical Intelligence

About

This interdisciplinary Summer School explores how Artificial Intelligence can be designed, developed, and implemented to prioritize human values, foster inclusion, and promote equity across diverse social contexts. It brings together a broad range of participants and speakers from the member universities of the Circle U. Alliance, creating a dynamic and collaborative learning environment. Participants will engage in lectures, workshops, case studies, and hands-on group projects, gaining both theoretical knowledge and practical tools to design AI systems that are inclusive, equitable, and human-centered. Collaborative sessions will encourage interdisciplinary exchange, creative thinking, and critical reflection on the societal role of AI.

Key focus area:

- Human-AI interaction and participatory design;
- Algorithmic bias, fairness, and accountability;
- Responsible and explainable AI;
- Policy, regulation, and societal implications of AI

Course leader

Prof. Francesco Marcelloni

Target group

This Summer School is tailored for:

- PhD students in computer science, computer engineering, data science, law, and social sciences;
- Postdoctoral researchers;
- Early-career researchers;
- Professionals working on ethical and responsible AI;
- Policymakers and practitioners involved in AI governance and social impact

Course aim

Participants will engage in lectures, workshops, case studies, and hands-on group projects, gaining both theoretical knowledge and practical tools to design AI systems that are inclusive, equitable, and human-centered.

Collaborative sessions will encourage interdisciplinary exchange, creative thinking, and critical reflection on the societal role of AI.

Expected Learning Outcomes:

- Understand foundational concepts of human-centered, inclusive, and equitable AI;
- Critically analyze algorithmic bias and its societal consequences;
- Engage with the societal and regulatory implications of AI;
- Collaborate across disciplines to address complex AI challenges

Fee info