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Panos Ipeirotis - Introduction

Crowdsourcing using Mechanical Turk Quality Management and Scalability Panos Ipeirotis – New York University. Panos Ipeirotis - Introduction. New York University, Stern School of Business. “A Computer Scientist in a Business School” http://behind-the-enemy-lines.blogspot.com/

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Panos Ipeirotis - Introduction

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  1. Crowdsourcing using Mechanical TurkQuality Management and Scalability Panos Ipeirotis – New York University

  2. 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

  3. 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 audience)PG (parental guidance) R(restricted)X (porn) • Cost/Speed Statistics • Undergrad intern: 200 websites/hr, cost: $15/hr

  4. Amazon Mechanical Turk: Paid Crowdsourcing

  5. 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 audience)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

  6. Bad news: Spammers! • Worker ATAMRO447HWJQ • labeled X (porn) sites as G (general audience)

  7. 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 numberof workers • Probability of correctness increases with quality of workers

  8. 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

  9. 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 • P[X → X]=9.847% P[X → G]=90.153% • P[G → X]=0.053% P[G → G]=99.947% Our “friend” ATAMRO447HWJQmainly marked sites as G.Obviously a spammer…

  10. 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

  11. After rejecting spammers, quality goes up • Spam keeps quality down • Without spam, workers are of higher quality • Need less redundancy for same quality • Same quality of results for lower cost Without spam 5 workers 94% correct Without spam 1 worker 80% correct With spam 11 workers 93% correct With spam 1 worker 70% correct

  12. Correcting biases • Classifying sites as G, PG, R, X • Sometimes workers are careful but biased • Classifies G → P and P → R • Average error rate for ATLJIK76YH1TF: too high • Error Rates for CEO of AdSafe • 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% Is she a spammer?

  13. 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 “reverse the errors” (technical details omitted) and separate error and bias • True error-rate ~ 9%

  14. Too much theory? Demo and Open source implementation available at: http://qmturk.appspot.com • 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 • Beta version, more improvements to come! • Suggestions and collaborations welcomed!

  15. Scaling Crowdsourcing: Use Machine Learning • Human labor is expensive, even when paying cents • Need to scale crowdsourcing • Basic idea: Build a machine learning model and use it instead of humans Data from existing crowdsourced answers New Case Automatic Model(through machine learning) Automatic Answer

  16. Tradeoffs for Automatic Models: Effect of Noise Get more data  Improve model accuracy Improve data quality  Improve classification Example Case: Porn or not? Data Quality = 100% Data Quality = 80% Data Quality = 60% Data Quality = 50% 20

  17. Scaling Crowdsourcing: Iterative training • Use machine when confident, humans otherwise • Retrain with new human input → improve model → reduce need for humans Automatic Answer Confident New Case Automatic Model(through machine learning) Not confident Get human(s) to answer Data from existing crowdsourced answers

  18. Tradeoffs for Automatic Models: Effect of Noise Get more data  Improve model accuracy Improve data quality  Improve classification Example Case: Porn or not? Data Quality = 100% Data Quality = 80% Data Quality = 60% Data Quality = 50% 22

  19. Scaling Crowdsourcing: Iterative training, with noise • Use machine when confident, humans otherwise • Ask as many humans as necessary to ensure quality Automatic Answer Confident New Case Automatic Model(through machine learning) Not confident for quality? Not confident Data from existing crowdsourced answers Get human(s) to answer Confident for quality?

  20. Thank you!Questions? “A Computer Scientist in a Business School” http://behind-the-enemy-lines.blogspot.com/ Email: panos@nyu.edu

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