Debate Schedule - July 15
11:00 AM - 11:30 AM : Welcome and Opening
11:30 AM - 12:30 PM : Rigor
Proposition: "Increased rigor would accelerate progress within the field, and the practices we might implement to increase rigor in the field do not present an overly costly trade-off against other values."
- Zack Lipton and Jacob Steinhardt (Affirmative) - "Troubling Trends in Machine Learning Scholarship"
- Katherine Lee (Affirmative) - "Submit to Journals"
- Alex Irpan (Negative) - "Some Papers Don't Reproduce, Should We Care?"
- James Bradbury (Negative) - Invited Participant
1:30 PM - 2:30 PM : Security
Proposition: "The vulnerabilities of present machine learning systems are so critical that we should not allow their general deployment in real-world settings."
- Aleksander Madry (Affirmative) - Invited Participant
- Alhussein Fawzi (Affirmative) - Invited Participant
- Percy Liang (Negative) - Invited Participant
- Aditi Raghunathan (Negative) - Invited Participant
2:30 PM - 3:30 PM : Fairness
Proposition: "To effectively contend with questions of fairness, the machine learning community cannot reduce fairness to a technical question. Instead, it must increasingly and explicitly adopt an agenda of broad institutional change and a stance on the political impacts of the technology itself.”
- Rodrigo Ochigame (Affirmative) - "Beyond Legitimation: Rethinking Fairness, Interpretability, and Accuracy in ML" (paper with Chelsea Barabas, Karthik Dinakar, Madars Virza, Joichi Ito)
- Ben Green (Affirmative) - "The Myth in the Methodology: Towards a Recontextualization of Fairness in ML" (paper with Lily Hu)
- Edward Raff (Negative) - "What About Applied Fairness?" (paper with Jared Sylvester)
- Alex Chouldechova (Negative) - Invited Participant
4:00 PM - 5:00 PM : Deep Learning
Proposition: “Current and foreseeable deep learning methods possess inherent limitations that place a ceiling on their ability to advance machine intelligence unless supplemented with other techniques.”