Artificial intelligence holds tremendous promise to benefit nearly all aspects of society, including healthcare, food production, economy, education, security, the law, and even our personal activities. The development of AI is creating new opportunities to improve the lives of people around the world. At the same time, these intelligent models may incorporate existing biases or create new biases that can seriously harm society. At its worst, AI can exacerbate misguided old practices and aggravate past social harms with its unprecedented processing powers and the veneer of seemingly objectivity, as humans in various social factions are disparately impacted by the AI-aided decisions. Facing the ethical implications of AI, students need to be prepared with the critical intellectual capacities that allow them to understand and deal with these ethical challenges. These capacities comprise multi-disciplinary concepts ranging from statistical learning theories, model design, and ethical foundation, to psychological and cultural frameworks that are necessary for successfully navigating and evaluating responsible AI practices. Further, "ethical competence" will involve understanding various challenges surrounding AI, such as ethical regulation, fairness assessment, and interpretability of models. Note that this is a technical class. Our focus will be on designing, evaluating, and mitigating bias in machine learning models. You will have three programming assignments in Python.

Academic Career: Undergraduate
Course Component: Lecture
Grade Component: LG/SNC Elective Basis
Course Requirements: PRE: CS1501 and (MATH 1180 OR MATH 0206 OR MATH 0280 OR MATH 1080 OR MATH 1181 OR MATH 1185)
Minimum Credits: 3
Maximum Credits: 3