66 citations · 141 across the 5 of their papers we have counts for
5 papers
Reputation Agent: Prompting Fair Reviews in Gig Markets
Carlos Toxtli, Angela Richmond-Fuller, Saiph Savage
Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider…
Becoming the Super Turker: Increasing Wages via a Strategy from High Earning Workers
Saiph Savage, Chun-Wei Chiang, Susumu Saito +2
Crowd markets have traditionally limited workers by not providing transparency information concerning which tasks pay fairly or which requesters are unreliable. Researchers believe…
Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms
Snehalkumar, S. Gaikwad, Durim Morina +35
Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores,…
Prototype Tasks: Improving Crowdsourcing Results through Rapid, Iterative Task Design
Snehalkumar "Neil" S. Gaikwad, Nalin Chhibber, Vibhor Sehgal +26
Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been…
Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms
Mark E. Whiting, Dilrukshi Gamage, Snehalkumar S. Gaikwad +25
Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts beha…