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We investigate the feasibility of obtaining highly trustworthy results usingcrowdsourcing on complex engineering tasks. Crowdsourcing is increasingly seenas a potentially powerful way of increasing the supply of labor for solvingsociety's problems. While applications in domains such as citizen-science,citizen-journalism or knowledge organization (e.g., Wikipedia) have seen manysuccessful applications, there have been fewer applications focused on solvingengineering problems, especially those involving complex tasks. This may be inpart because of concerns that low quality input into engineering analysis anddesign could result in failed structures leading to loss of life. We comparedthe quality of work of the anonymous workers of Amazon Mechanical Turk (AMT),an online crowdsourcing service, with the quality of work of expert engineersin solving the complex engineering task of evaluating virtual wind tunnel datagraphs. On this representative complex engineering task, our results showedthat there was little difference between expert engineers and crowdworkers inthe quality of their work and explained reasons for these results. Along withshowing that crowdworkers are effective at completing new complex tasks ourpaper supplies a number of important lessons that were learned in the processof collecting this data from AMT, which may be of value to other researchers.

Lessons Learned from an Experiment in Crowdsourcing Complex Citizen Engineering Tasks with Amazon Mechanical Turk