Human-centered Machine Learning (2018 Spring)
Insturctor: Chenhao Tan contact
Office hours: 3:30-4:30 on Monday, 4:30-5:30 on Wednesday, or by appointment (ECES 118A)
- Location and time: ECES 112, 1:00-2:15pm on Mondays and Wednesdays
- Syllabus (Must READ if you are taking the course)
Please finish these questions and submit by the second week of the semester. This does not contribute to your final scores, but it helps both yourself and the instructors to know your mathematical background.
Programming: You are expected to know how to program and finish a final project.
- proposals (2 times, 20%)
- course presentation (10%)
- final project (50%)
- peer feedback (15%)
- participation (5%)
Week 2: You are not so Smart
- Jan 22, Human Decisions and Machine Predictions.
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan.
Quarterly Journal of Economics, 2018.
- Jan 24, The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter. Chenhao Tan, Lillian Lee, Bo Pang. In Proceedings of ACL, 2014.
Week 3: Open the Black Box (Interpretable Machine Learning)
- Jan 29, Why should I trust you?: Explaining the Predictions of Any Classifier. Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin. In Proceedings of KDD, 2016.
- Jan 31, Examples are not Enough, Learn to Criticize!
Criticism for Interpretability. Been Kim, Rajiv Khanna, Oluwasanmi Koyejo. In Proceedings of NIPS, 2016.
- DeepXplore: Automated Whitebox Testing of Deep Learning Systems, Pei, Cao, Yang and Jana. In Proceedings of SOSP, 2017.
- Interactive and Interpretable Machine Learning Models for Human Machine Collaboration, Been Kim, PhD thesis.
- The Mythos of Model Interpretability, Zachary C. Lipton.
- Rationalizing Neural Predictions, Tao Lei, Regina Barzilay and Tommi Jaakkola. In Proceedings of EMNLP, 2016.
- Learning Explanatory Rules from Noisy Data. Richard Evans, Edward Grefenstette. Journal of Artificial Intelligence Research, 2018.
- Network Dissection: Quantifying Interpretability of Deep Visual Representations. David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba. In Proceedings of CVPR 2017.
Week 4: Human-in-the-loop Machine Learning
- Feb 5, Interactive Machine Learning. Jerry Alan Fails, Dan R. Olsen, Jr. In Proceedings of IUI, 2003.
- Feb 7, Learning with Humans in the Loop. Thorsten Joachims. ECML keynote talk, 2013.
- Visual Recognition with Humans in the Loop. Steve Branson, Catherine Wah, Florian Schroff, Boris Babenko, Peter Welinder, Pietro Perona, Serge Belongie. In Proceedings of ECCV, 2010.
- Flock: Hybrid Crowd-Machine Learning Classifiers. Justin Cheng, Michael S. Bernstein. In Proceedings of CSCW, 2015.
- Power to the People: The Role of Humans in Interactive Machine Learning. Saleema Amershi, Maya Cakmak, William Bradley Knox, Todd Kulesza. AI Magazine, 2014.
Week 5: Machine-in-the-loop Interactions
Week 6: First Proposal
- Feb 19, presentation & discussion
- Feb 21, presentation & discussion
First proposal peer feedback due on Feb 23
Week 7: Machine-in-the-loop application: Creative Writing
Week 8: Machine-in-the-loop application: Decision Making
- Mar 5, Assessing Human Error Against a Benchmark of Perfection. Ashton Anderson, Jon Kleinberg, Sendhil Mullainathan. In Proceedings of KDD, 2016.
- Mar 7, Predicting the knowledge–recklessness distinction in the human brain. Iris Vilares, Michael J. Wesley, Woo-Young Ahn, Richard J. Bonnie, Morris Hoffman, Owen D. Jones, Stephen J. Morse, Gideon Yaffe, Terry Lohrenz, and P. Read Montague. PNAS, 2016.
Week 9: Second Proposal
- Mar 12, presentation & discussion
- Mar 14, presentation & discussion
Second proposal peer feedback due on Mar 16
Week 10: Machine-in-the-loop application: Categorization & Education
Week 11: Spring break
Week 13: Machine-in-the-loop application: Accessibility
Week 12: Midpoint project presentation
- Apr 9, free time to work on your projects
- Apr 11, presentation
Project peer feedback due on Apr 13
Week 14: Machine-in-the-loop application: Robotics
- Apr 16, Human-robot proxemics: Physical and psychological distancing in human-robot interaction. Jonathan Mumm, Bilge Mutlu. In Proceedings of HRI, 2011.
- Apr 18, Robotic Assistance in Coordination of Patient Care. Matthew Gombolay, Jessie Yang, Bradley Hayes, Nicole Seo, Samir Wadhwania, Zixi Liu, Tania Yu, Neel Shah, Toni Golen, Julie Shah. In Proceedings of RSS, 2016.
Week 15: Fairness, Accountability, and Transparency
- Apr 23, Inherent Trade-Offs in the Fair Determination of Risk Scores. Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan. In Proceedings of ITCS, 2017.
- Apr 25, Algorithmic decision making and the cost of fairness. Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, Aziz Huq. In Proceedings of KDD, 2017.
Guest instructor: Raf Frongillo.
Week 16: Final Project Presentation
- Apr 30, presentation
- May 2, presentation
Final project report due on May 2