Core Modules
Responsible Artificial Intelligence I & II
These modules introduce the ethical theories, accountability frameworks, and privacy principles essential
to
responsible AI. The curriculum covers moral frameworks, designing AI with ethical agents, and balancing
privacy
with functionality.
Key Topics:
- Ethical AI design
- Algorithmic accountability
- Privacy principles
- Fairness in AI
- Security protocols
Practical Applications:
- Case studies on biased algorithms
- Ethical decision-making scenarios
- Methods to design transparent AI systems
Human-Centered Design and User Experience
This module series focuses on user-centered design principles and usability testing, preparing students
to create
intuitive and accessible AI solutions. Key areas include requirement engineering and interface design.
Key Topics:
- User requirements
- Usability testing
- Interface and interaction design
- User experience (UX)
Practical Applications:
- Designing mock-ups
- Prototyping user-centered interfaces
- Testing user interaction with AI systems
Cybersecurity and Data Privacy
This module covers the fundamental and advanced aspects of securing AI systems and managing data privacy.
The
curriculum emphasizes data protection regulations and best practices for maintaining user trust.
Key Topics:
- Data encryption
- Privacy by design
- Regulatory compliance (e.g., GDPR)
- Risk assessment
Practical Applications:
- Projects on secure data management
- Building privacy-compliant AI systems
- Implementing cybersecurity measures for AI
Ethics and Professionalism in Data Science
This module addresses ethical considerations in data science, including discrimination, bias, and privacy
concerns,
and helps students develop a critical approach to fairness in AI.
Key Topics:
- Ethical frameworks
- Discrimination prevention
- Professional responsibility
- Fairness metrics
Practical Applications:
- Assessing fairness in data science
- Applying ethical frameworks to real-world AI
- Developing bias-mitigating solutions
Natural Language Processing (NLP) and Human-Centered AI
This module covers human-centered NLP, focusing on the design of language models that prioritize
interpretability
and user comprehension. Advanced NLP topics, including deep learning techniques, are also explored.
Key Topics:
- Syntactic parsing
- Machine translation
- Attention models
- Deep neural networks
Practical Applications:
- Building and testing NLP models
- Creating interpretable AI language systems
- Applying NLP in sentiment analysis
Advanced Modules
Explainable Artificial Intelligence (XAI)
This module focuses on making complex AI models interpretable for end-users. Students learn methods to
present AI-driven insights transparently.
Key Topics:
- Model interpretability
- Data visualization
- Explainability tools
Practical Applications:
- Creating reports that explain model behavior
- Developing visualization tools for AI outputs
- Designing interpretable AI applications for healthcare and finance
Equity and Discrimination in Computing Systems
This module addresses bias and discrimination in computing systems, focusing on ethical machine learning
practices.
Key Topics:
- Fairness metrics
- Bias identification
- Anti-discrimination strategies
Practical Applications:
- Analyzing case studies on biased AI
- Using fairness metrics to assess model performance
- Designing unbiased ML algorithms
Research Data Management and Sharing
This module explores the management, archiving, and sharing of research data, ensuring data handling
aligns with best practices.
Key Topics:
- Data archiving
- Open-access policies
- Reproducibility
- Data sharing ethics
Practical Applications:
- Developing data management plans
- Archiving research data
- Creating frameworks for responsible data sharing