Milestone 9 Hawkeye

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Ideas for Foundation 2: Reviewers Money Deposit (RMD)

This addresses the challenge in case of payment to the reviewers. So, we explain the system using an algorithm as follows:

  1. From every given work by a requestor, we assume the worker is willing to pay X amount for the completion of the work. We also assume that we have 6 reviewers, 3 to review the request and 3 to review the work done.
  2. M% of this X amount is deducted and added to the RMD which belongs to the platform. And the rest (X-0.0M) amount is displayed to the worker as the payment for the job.
  3. For every job that has been put up (M/6)% is taken from the RMD and given as a payment to the reviewer of the request. If the request is acceptable, then it this published. Else the rest M/3 % which is supposed to be used by the work reviewers is given back to the requestor, and the work is rejected. This process should be continued till the request is accepted by the reviewer.
  4. Once the work is completed by the worker, the reviewer reviews the work. If the work is not satisfactory then the amount is paid to the reviewer but is sent back to the worker for improvisation. So the next time the worker comes for a review, then M/3% of the payment is reduced in order to pay the reviewers.


Ideas for Foundation 4:

According to the fields of expertise, we divide the crowd workers into pools. This fields of expertise, for example, crowdsourcing, data mining, web services, algorithm, etc. These field of expertise are the broad areas the job may be classified under. Every job should be tagged under fields to which it belongs to. Assigning the area of expertise to the worker Depending on the history and quality of the work done in a particular area, the worker is deemed as an expert in that area. Choosing the workers to be reviewers: Assuming that there are multiple teams for reviewing, each consisting of a three representatives of requestors and three representatives of workers. These representatives are chosen based on:

  • Area of expertise
  • Rating for the work done
  • Rating for the reviews given previously

From each pool of workers, who are experts in a particular area, we chose a fixed number of workers who can be reviewers. The teams are formed in advance depending taking a mix bag of experts from different fields. Once the job is published reviewing team is assigned based on the fields it is tagged under.