AI - Artificial Intelligence

This course introduces foundational concepts and methods in artificial intelligence, including but not limited to supervised and unsupervised learning, deep learning, and reinforcement learning. The course highlights practical applications of emerging AI techniques in domains such as computer vision, natural language processing, and decision support, enabling students to connect theory with real-world use cases. Emphasis is placed on assessing the suitability, assumptions, and limitations of AI models for specific contexts. Learners will also consider the ethical and practical implications of AI deployment, including model bias and fairness, the possibility of discrimination when AI systems are trained on non-inclusive data, and examples of AI use in Indigenous language preservation.

Students will identify and discuss ethical considerations and responsible AI practices through case-based learning and virtual seminars; evaluate real-world AI applications and their societal implications using structured peer discussions and ethical impact assessments of contemporary AI systems; and demonstrate the ethical use of AI-generated outputs in practical contexts through reflective essays and collaborative analysis to develop genuine ethical reasoning capabilities. Topics will include Indigenous data sovereignty, how lack of inclusion may lead to harm, and how ethical, participatory AI design can help mitigate and prevent this.