• Publications
  • Influence
TurkScanner: Predicting the Hourly Wage of Microtasks
TLDR
This study explores various computational methods for predicting the working times (and thus hourly wages) required for tasks based on data collected from other workers completing crowd work, and explores the challenge of accurately recording working times both automatically and by asking workers. Expand
Becoming the Super Turker:Increasing Wages via a Strategy from High Earning Workers
TLDR
This paper explores how novice workers can improve their earnings by following the transparency criteria of Super Turkers, i.e., crowd workers who earn higher salaries on Amazon Mechanical Turk (MTurk), and highlights that tool development to support crowd workers should be paired with educational opportunities that teach workers how to effectively use the tools. Expand
Understanding Interface Design and Mobile Money Perceptions in Latin America
TLDR
This paper analyzed 27 mobile money applications on the market and investigated the perceptions that people in Latin America have of such interfaces, singled out the interface features that have the greatest influence in user adoption in developing countries. Expand
Exploring Blockchain for Trustful Collaborations between Immigrants and Governments
TLDR
It is identified that for Mexican immigrants having clear workflows of how their money flows and a sense of control over this workflow is important for collaborating with their government. Expand
Crowd Coach
TLDR
Crowd Coach, a system that enables workers to receive peer coaching while on the job, is presented and it is found that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks. Expand
Crisis Management in the Taiwan Strait
Abstract : China is getting stronger. The United States is the only superpower strengthening alliance to defeat global terrorism and work to prevent attacks against US and her friends. Historically,Expand
Crowd Work on a CV? Understanding How AMT Fits into Turkers' Career Goals and Professional Profiles
TLDR
This work extends existing understandings of who crowd workers are and why they crowd work by seeking to better understand how crowd work factors into Turkers' professional profiles, and how it can subsequently better support crowd workers in their career advancement. Expand
Predicting the Working Time of Microtasks Based on Workers' Perception of Prediction Errors
TLDR
A computational technique for predicting microtask working times based on past experiences of workers regarding similar tasks and challenges encountered in defining evaluation and/or objective functions have been described based on the tolerance demonstrated by workers with regard to prediction errors. Expand
You’d Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift
TLDR
It is found that people rely on machine learning models more when making decisions on out-of-distribution data than in-dist distribution data, and enabling people to visualize the data distributions and the model’s performance does not seem to help. Expand
Turker Tales: Integrating Tangential Play into Crowd Work
TLDR
Turker Tales, a Google Chrome extension that uses tangential play to encourage crowd workers to write, share, and view short tales as a side activity to their main job on Amazon Mechanical Turk, is presented. Expand
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