Key Areas of Focus
Our Master’s program in HCAI emphasizes critical areas essential for developing AI systems that are ethical, transparent, and centered around human needs. Each focus area addresses unique technical, social, and ethical challenges in modern AI.
Algorithmic Fairness and Accountability
Developing algorithms that adhere to principles of fairness, preventing biases and discrimination in automated decision-making. This area focuses on methods to design and evaluate systems that are fair, transparent, and understandable.
Skills Gained: Students learn to identify biases in data, build responsible predictive models, and implement techniques to ensure that algorithmic decisions meet ethical standards.
Real-World Applications: Used in sectors such as justice, healthcare, and finance, where accurate and impartial decisions are essential.
Cybersecurity and Data Privacy
With the rise of data collection, protecting personal information is crucial. This area explores advanced techniques to ensure data security and privacy, as well as compliance with regulations like GDPR.
Skills Gained: Students gain expertise in encryption techniques, data management, and best practices to secure personal information from unauthorized access.
Real-World Applications: Vital for organizations managing large volumes of personal data, such as hospitals, banks, and online platforms, where information security is a top priority.
Human-Centered Design and User Experience (UX)
Designing AI systems that are intuitive and easy for end-users, with a focus on positive human-machine interaction. This area covers user-centered design principles and UX testing strategies.
Skills Gained: Ability to create AI interfaces that meet user needs, UX testing techniques, and inclusive design principles to ensure universal accessibility.
Real-World Applications: Essential in healthcare, education, and customer service projects where AI must integrate seamlessly without frustrating or confusing users.
Explainable Artificial Intelligence (XAI)
Making AI models understandable and transparent is key to building user trust. This area explores approaches to explain AI decisions in ways that are interpretable, even for non-experts.
Skills Gained: Proficiency in making complex models accessible, creating interpretable reports, and using visualization techniques to communicate AI processes effectively.
Real-World Applications: Important in areas like finance, healthcare, and policy, where stakeholders need to understand AI-driven decisions and trust them.