teaching and mentoring info

HDSI Capstone 2023-2024 and 2024-2025 Course Description

There is a growing societal concern over the potential and real negative effects of AI, particularly in terms of fairness and explainability. This concern is considered in this course where students will study high-profile cases of algorithmic discrimination, explore different definitions and metrics of AI fairness, and understand their practical implications. The challenge lies in translating these complex concepts into real-world applications, training students to independently analyze AI fairness and explainability, and emphasizing the societal impact of these issues. The course aims to equip students with skills to assess algorithmic fairness, understand data limitations, and apply bias mitigation techniques in AI models. Students will explore the ethical dimensions of artificial intelligence (AI), with a specific focus on fairness assessments and bias mitigation. This course integrates practical workshops, case studies, include IBM AI Fairness 360 Model Overview and the evaluation of model bias using Medical Expenditure data. Through lectures, workshops, readings, and hands-on projects, students will gain an understanding of how to assess algorithmic fairness, measure fairness metrics, and identify the limitations of data in capturing fairness. They will also learn techniques for mitigating bias in AI models through pre-, in-, and post-processing. The course will emphasize real-world applications and the impact of ethical AI considerations on different stakeholders. Students will engage in replication projects and independent analyses to develop their skills in fairness assessments and bias mitigation.

The first quarter consists of replicating an existing research project, whereas the second quarter is a group-based research project of the students’ choice. We utilize IBM’s AIF360 to learn about fairness metrics and bias mitigation techniques in practice. See the projects section for past student projects! 🖼️