New York Mechanical Turk Meetup

Panos Ipeirotis - Introduction  New York University, Stern School of Business “A Computer Scientist in a Business School” http://behind-the-enemy-lines.blogspot.com/ Email: panos@nyu.edu © 2009 Amazon.com, Inc. or its Affiliates.

Example: Build an Adult Web Site Classifier  Need a large number of hand-labeled sites  Get people to look at sites and classify them as: G (general), PG (parental guidance), R (restricted), X (porn) Cost/Speed Statistics  Undergrad intern: 200 websites/hr, cost: $15/hr  MTurk: 2500 websites/hr, cost: $12/hr © 2009 Amazon.com, Inc. or its Affiliates.

Improve Data Quality through Repeated Labeling  Get multiple, redundant labels using multiple workers  Pick the correct label based on majority vote 11 workers 93% correct 1 worker 70% correct  Probability of correctness increases with number of workers  Probability of correctness increases with quality of workers © 2009 Amazon.com, Inc. or its Affiliates.

But Majority Voting is Expensive Single Vote Statistics  MTurk: 2500 websites/hr, cost: $12/hr  Undergrad: 200 websites/hr, cost: $15/hr 11-vote Statistics  MTurk: 227 websites/hr, cost: $12/hr  Undergrad: 200 websites/hr, cost: $15/hr © 2009 Amazon.com, Inc. or its Affiliates.

Using redundant votes, we can infer worker quality  Look at our spammer friend ATAMRO447HWJQ together with other 9 workers  We can compute error rates for each worker Error rates for ATAMRO447HWJQ Our “friend” ATAMRO447HWJQ  P[X → X]=9.847% P[X → G]=90.153% mainly marked sites as G.  P[G → X]=0.053% P[G → G]=99.947% Obviously a spammer… © 2009 Amazon.com, Inc. or its Affiliates.

Rejecting spammers and Benefits Random answers error rate = 50% Average error rate for ATAMRO447HWJQ: 45.2%  P[X → X]=9.847% P[X → G]=90.153%  P[G → X]=0.053% P[G → G]=99.947% Action: REJECT and BLOCK Results:  Over time you block all spammers  Spammers learn to avoid your HITS  You can decrease redundancy, as quality of workers is higher © 2009 Amazon.com, Inc. or its Affiliates.

After rejecting spammers, quality goes up  Spam keeps quality down  Without spam, workers are of higher quality Without spam  Need less redundancy for same quality 5 workers  Same quality of results for lower cost 94% correct Without spam 1 worker With spam 80% correct 11 workers 93% correct With spam 1 worker 70% correct © 2009 Amazon.com, Inc. or its Affiliates.

Correcting biases  Classifying sites as G, PG, R, X  Sometimes workers are careful but biased Error Rates for Worker: ATLJIK76YH1TF P[G → G]=20.0% P[G → P]=80.0% P[G → R]=0.0% P[G → X]=0.0% P[P → G]=0.0% P[P → P]=0.0% P[P → R]=100.0% P[P → X]=0.0% P[R → G]=0.0% P[R → P]=0.0% P[R → R]=100.0% P[R → X]=0.0% P[X → G]=0.0% P[X → P]=0.0% P[X → R]=0.0% P[X → X]=100.0%  Classifies G → P and P → R  Average error rate for ATLJIK76YH1TF: 45.0% Is ATLJIK76YH1TF a spammer? © 2009 Amazon.com, Inc. or its Affiliates.

Correcting biases Error Rates for Worker: ATLJIK76YH1TF P[G → G]=20.0% P[G → P]=80.0% P[G → R]=0.0% P[G → X]=0.0% P[P → G]=0.0% P[P → P]=0.0% P[P → R]=100.0% P[P → X]=0.0% P[R → G]=0.0% P[R → P]=0.0% P[R → R]=100.0% P[R → X]=0.0% P[X → G]=0.0% P[X → P]=0.0% P[X → R]=0.0% P[X → X]=100.0%  For ATLJIK76YH1TF, we simply need to compute the “non- recoverable” error-rate (technical details omitted)  Non-recoverable error-rate for ATLJIK76YH1TF: 9% © 2009 Amazon.com, Inc. or its Affiliates.

Too much theory? Open source implementation available at: http://code.google.com/p/get-another-label/  Input: – Labels from Mechanical Turk – Cost of incorrect labelings (e.g., XG costlier than GX)  Output: – Corrected labels – Worker error rates – Ranking of workers according to their quality  Alpha version, more improvements to come!  Suggestions and collaborations welcomed! © 2009 Amazon.com, Inc. or its Affiliates.

New York Mechanical Turk Meetup

by Panos Ipeirotis on Aug 28, 2009

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New York Mechanical Turk Meetup — Presentation Transcript