What is this course about?Questions we will explore:
- Why do we care about interpretable AI? Why do we care about fair AI? Real-life examples.
- What are model explanations? What is fair Artificial intelligence?
- Given a black box AI model, how do you explain it? Fundamental and State-of-the-art method taxonomies.
- Given an application, what type of model should you use (interpretability vs performance trade-off)?
- When a model is un'fair', what can you do?
- Regulatory trends and societal perspectives on interpretable and fair AI.
Topic: As black-box AI models grow increasingly relevant in human-centric applications, explainability and fairness becomes increasingly necessary for trust in adopting AI models. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-the art methods to enable students to apply to future work. Key technical topics include surrogate methods, feature visualization, network dissection, adversarial debiasing, and fairness metrics. There will be a survey of recent legal and policy trends.
From this course, students will receive an overview of some of the most important recent methods on how to explain AI models, how it's currently done, how to choose models based on level of interpretability needed, and relevant user implications of interpretability and fairness in applications such as healthcare, facial recognition, and regulatory decision-making.
Format: Each week a guest lecturer from AI research, industry, and related policy fields will present an open problem and solution, followed by a short roundtable Q&A/discussion with the class.
Participation: This class is offered C/NC. You should attend at least 6 sessions to receive credit. Given the time zone differences + extra challenges that come with this quarter, we’ll make accommodations for lecture attendance on video watching instead for C/NC if you reach out to us. We strongly encourage attending class to get the most out of interactive, small, seminar style lectures, but definitely recognize the additional challenges that come with this quarter. Since we keep the class size small to facilitate discussion with guest lecturers, auditing may be approved on a case-by-case basis depending on student numbers, with permission from the course staff.
Contact: If you have questions about the class, please email firstname.lastname@example.org.