Joseph Chee Chang, Saleema Amershi, Ece Kamar. CHI 2017.
work done during internship at Microsoft Research, Redmond.
Generating comprehensive labeling guidelines for crowdworkers can be challenging for complex datasets. Revolt harnesses crowd disagreements to identify ambiguous concepts in the data and coordinates the crowd to collaboratively create rich structures for requesters to make post-hoc decisions, removing the need for comprehensive guidelines and enabling dynamic label boundaries.