Human computation is a research area that studies how to build intelligent systems that involve humans, performing computation that challenges even the most sophisticated AI algorithms that exist today. Well-known examples of human computation systems include crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) that coordinate workers to perform tasks for monetary tasks, games with a purpose (e.g., the ESP Game) that generate useful data through gameplay, and identity verification systems (e.g., reCAPTCHA) that accomplish remarkable feats through billions of users performing computation for access to online content.
A prevalent assumption in crowdsourcing is the idea that the tasks must be trivially solvable by any person with basic perceptual abilities and common-sense knowledge. However, some tasks are complex and require expertise to solve or decompose. Scientific data processing is a prime example, where the objects of interest (e.g., images of micro-organisms, sleep EEG signals, genetic sequences) are often unfamiliar to those without formal training. Likewise, the task of transforming dense medical information into correct, readable and comprehensible summaries raises similar challenges. In this talk, I will draw specific examples from my previous work attempting to push the state-of-the-art of crowdsourcing systems towards solving more complex tasks. I will end by describing my current research projects on mixed-expertise crowdsourcing -- Curio, a platform that enables researchers in the natural, medical and social sciences, who are domain experts but not necessarily technically savvy, to launch and manage crowdsourcing projects with minimal effort, and SimplyPut, a crowdsourcing platform for improving health literacy through the collaborative summarization of medical information. Underlying both projects is a new model for crowdsourcing that combines the skills of small expert communities with the scale of large, anonymous crowds.
Edith Law is a CRCS postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University. She graduated from Carnegie Mellon University in 2012 with Ph.D. in Machine Learning, where she studied human computation systems that harness the joint efforts of machines and humans. She is a Microsoft Graduate Research Fellow, co-authored the book "Human Computation" in the Morgan & Claypool Synthesis Lectures on Artificial Intelligence and Machine Learning, co-organized the Human Computation Workshop (HCOMP) Series at KDD and AAAI from 2009 to 2012, and helped create the first AAAI Conference on Human Computation and Crowdsourcing. Her work on games with a purpose and large-scale collaborative planning has received best paper honorable mentions at CHI.
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