CS 591 — FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY IN AI, FALL 2020

Wednesdays (Moved from Mondays), 6:30–9:15 PM EST Online via Zoom (Lectures will be recorded)

Instructor: Mehrnoosh Sameki

Course Description: Enabling the responsible development of artificial intelligence technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Now there are calls from across the industry (academia, government, and industry leaders) for technology creators to ensure that AI is used only in ways that benefit people and "to engineer responsibility into the very fabric of the technology." Overcoming these challenges and enabling responsible development is essential to ensure a future where AI and machine learning can be widely used across different domains. This course will pursue a cross-disciplinary investigation of several areas under the responsible AI umbrella (fairness, interpretability, and accountability). Students will learn about state-of-the-art research and best practices in the covered domains and use available open-source fairness and interpretability toolkits to apply their learnings to publicly available datasets from healthcare, finance, and other domains.

Course Format: This course is a weekly meeting in which participants discuss recent and important concepts and results in the area of responsible AI research/practice. For a typical meeting, I will spend the first ~75 minutes of the session presenting on the listed topic of that week. We will next discuss 1 or 2 relevant papers that are selected for discussion according to the session's focus topic. Students must read each week's selected papers prior to the class and submit a one page summary of main takeaways for each paper.

Objectives: To increase participants' familiarity with recent and important research results in responsible AI and in particular AI accountability, interpretability, and fairness; to improve participants' skills in presenting and discussing relevant topics.

Grading policy: 30% reading assignments summary, 30% mid-semester project, 40% final project
Final course scores represent the following grades (scores are rounded to the nearest integer):
A (94-100%)
A- (90-93%)
B+ (87-89%)
B (84-86%)
B- (80-83%)
C+ (77-79%)
C (74-76%)

Schedule

Week Date Topics Slides Papers
1 9/14 Class Intro
General Introduction to Responsible AI
Lecture Slides No Paper Presentations
2 9/21 Overview of Machine Learning Interpretability
Lecture Slides
Chapters 1 and 2
Paper 1:
Machine Learning Interpretability: A Survey on Methods and Metrics Diogo V. Carvalho et al.
3 9/30 Training Interpretable (Glassbox) Models Slides TBA, Chapter 4 Paper 1:
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. Vera Liao et al.
Paper 2:
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs Sungsoo Ray Hong et al.
4 10/5 Explaining Black-Box Models Slides TBA, Chapter 5 Paper 1:
Explainable Machine Learning in Deployment Umang Bhatt et al.
Paper 1:
Robustness in Machine Learning Explanations: Does It Matter? Leif Hancox-Li et al.
5 10/13 - Interpretability: Connections with Debugging
- Evaluating Interpretability
Slides TBA Paper 1:
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez et al.
Paper 2:
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh et al.
6 10/19 Interpretability in Industry
Guest Speakers
Slides TBA Paper 1: TBD
Paper 2: TBD
6 10/26 Overview of Machine Learning Fairness Slides TBA Paper 1:
Big Data's Disparate Impact Solon Barocas et al.
Paper 2:
Fairness and Abstraction in Sociotechnical Systems Andrew Selbst et al.
8 11/2 Fairness in Practice Slides TBA Paper 1:
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? Ken Holstein et al.
Paper 2:
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. Michael Madaio et al.
8 11/9 Fairness Assessment and Unfairness Mitigation Slides TBA Paper 1:
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi et al.
10 11/16 Ethics in Industry
Student Presentations (Midterm Projects)
Slides TBA Paper 1:
Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics Jacob Metcalf et al.
11 11/23 Accountability in AI Slides TBA Paper 1:
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches Kasper Sokol et al.
Paper 2:
Accountability of AI Under the Law: The Role of Explanation Finale Doshi-Velez et al.
12 11/30 Guidelines for Human-AI Interaction Slides TBA Paper 1:
Guidelines for Human-AI Interaction Saleema Amershi et al. Paper 2: TBD
13 12/7 Student Final Project Presentations Slides TBA Student Final Project Presentations