• Accepted Papers

View a list of accepted papers.

  • Keynotes

Find detailed information about the keynotes.

  • HCOMP-19 Preliminary Technical Program

Monday, Oct 28, 2019

9am-5:30pm Workshops and Crowdcamp , Doctoral Symposium
6:30pm Conference Dinner and Fireside Chat (location: TBD)

Fireside Chat Panelists: Jane Yung-jen Hsu, Paul Bennett, Foster Provost, and Panos Ipeirotis

Tuesday Oct 29, 2019

Sessions location: TBD

9am-10am Keynote 1: Been Kim (Google Brain)
10am-10:30am Coffee Break

Session 1: Human-AI Collaboration

Chair: TBD
Beyond Accuracy: On the Role of Mental Models in Human-AI Teams Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel Weld, Walter Lasecki and Eric Horvitz
A Hybrid Approach to Identifying Unknown Unknowns of Predictive Models Colin Vandenhof
Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates Nikhil Garg, Lodewijk Gelauff, Sukolsak Sakshuwong and Ashish Goel
Second Opinion: Supporting last-mile person identification with crowdsourcing and face recognition Vikram Mohanty, Kareem Abdol-Hamid, Courtney Ebersohl and Kurt Luther
12:00-1:30pm Lunch

Session 2: Explanations, Interpretability and Fairness

Chair: TBD
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran and Giedrius Burachas
The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems Mahsan Nourani, Samia Kabir, Sina Mohseni and Eric Ragan
Human Evaluation of Models Built for Interpretability Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel Gershman and Finale Doshi-Velez
Interpretable Image Recognition with Hierarchical Prototypes Peter Hase, Chaofan Chen, Oscar Li and Cynthia Rudin
How Do We Talk about Other People? Group (Un)Fairness in Natural Language Image Descriptions Jahna Otterbacher, Pınar Barlas, Styliani Kleanthous and Kyriakos Kyriakou
3:10pm-3:40pm Coffee Break

Session 3: Task Design

Chair: TBD
Not Everyone Can Write Great Examples But Great Examples Can Come From Anywhere Shayan Doroudi, Ece Kamar and Emma Brunskill
Testing Stylistic Interventions to Reduce Emotional Impact of Content Moderation Workers Sowmya Karunakaran and Rashmi Ramakrishnan
Understanding the Impact of Text Highlighting in Crowdsourcing Tasks Jorge Ramirez, Marcos Baez, Fabio Casati and Boualem Benatallah
Platform-related Factors in Repeatability and Reproducibility of Crowdsourcing Tasks Rehab Qarout, Alessandro Checco, Gianluca Demartini and Kalina Bontcheva
5:00pm-6:30pm Work-in-Progress/Demo Session

Wednesday Oct 30, 2019

Sessions location: TBD

9am-10am Keynote 2: Rumi Chunara (New York University)
10am-10:30am Coffee Break

Session 4: Recruiting the Crowd

Chair: TBD
What You See is What You Get? The Impact of Representation Criteria on Human Bias in Hiring Andi Peng, Besmira Nushi, Emre Kiciman, Kori Inkpen, Siddharth Suri and Ece Kamar
Fair Work: Crowd Work Minimum Wage with One Line of Code Mark Whiting, Grant Hugh and Michael Bernstein
AI-based Request Augmentation to Increase Crowdsourcing Participation Junwon Park, Ranjay Krishna, Pranav Khadpe, Li Fei-Fei and Michael Bernstein
A Large-Scale Study of the "Wisdom of Crowds'" Camelia Simoiu, Chiraag Sumanth, Alok Shankar and Sharad Goel
12:00-1:30pm Lunch
1:30-2:30pm Townhall
2:30pm-3:00pm Coffee Break

Session 5: Collecting and Learning from Data

Chair: TBD
Going Against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis Yan Shvartzshnaider, Noah Apthorpe, Nick Feamster and Helen Nissenbaum
Progression In A Language Annotation Game With A Purpose Chris Madge, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Silviu Paun and Massimo Poesio
Gamification of Loop-Invariant Discovery from Code Andrew Walter, Benjamin Boskin, Seth Cooper and Panagiotis Manolios
Crowdsourced PAC Learning under Classification Noise Shelby Heinecke and Lev Reyzin
Learning to Predict Population-Level Label Distributions Tong Liu, Pratik Sanjay Bongale, Akash Venkatachalam and Christopher Homan