In April 2012, We held a private reading meeting for NIPS 2011.

I read “Iterative Learning for Reliable Crowdsourcing Systems” [Karger+ NIPS11].

**[Karger+ NIPS11] Iterative Learning for Reliable Crowdsourcing Systems**

This paper targets Amazon Mechanical Turk(AMT) which separates a large task into microtasks. Each worker in AMT may be a spammer (who answers randomly to earn fee) or a hammer (who answers correctly).

This paper’s model simply assumes that each microtask has a coherent binary answer, each worker has probability to answer correctly which is independent on tasks. On the assumption, it estimates an average error rate when task size is enough large.

I don’t mind it needs simple strong assumption, but I’m sorry the model parameter q can’t be known its true value so that a practical problem can’t fit the model if the assumption was accepted.