WinterMilestone 3 BPHC ReputationIdea : Referral Network for Workers
One of the major needs identified in existing platforms for crowd-sourcing is the ability of the platform to effectively match capable workers with suitable jobs or HITs (Human Intelligence Tasks). The Stanford Crowd Research Team has thus far developed their Daemo Platform, which makes a significant step in meeting this requirement through the Boomerang Ranking System.
We propose a referral based system that deals with the following reputation and relevant work issues:
- A problem with a system that prefers assigning tasks to workers with high reputation is that new workers may find it difficult to find good HITs. Requesters would tend to allot jobs to the same set of highly rated users. Similarly, existing workers would tend to take up HITs from the same set of requesters with high ratings.
- Workers rely on external forums (TurkerNation, Reddit etc) to find good HITs from reliable requesters. Clearly any future crowdsourcing platform should reduce this burden on the worker by allowing dedicated workers to easily share good HITs and help fellow workers to find relevant work .
Referral Network for Workers
The employee referral network in the corporate world is known to be a cost and time effective method of recruitment that produces high quality candidates. 92% of the participants in the Global Employee Referral Index 2013 Survey stated that referrals were a top source of recruitment. We thought about adapting the referral system to our crowd sourcing platform design. This would enable workers to recommend or “refer” other workers (existing or new) for good HITs.
Implementation of a Referral Network for Crowdsourcing
We illustrate the use of the referral system through the following example.
- 1. Requester R has a HIT to post on the crowdsourcing platform. R can view highly rated workers using Boomerang and make the HIT visible to them (or post the HIT publicly for all to see).
- 2. Worker W notices the HIT posted by R and has the following options:
- * Accept the HIT
- * Share the HIT with fellow workers. In the referral based scheme, this can be done in the following ways:
W refers workers he or she knows to R.
- * W can publicly offer to refer anyone who is interested in HIT. This is similar to how referrals are shared across social networks like Facebook, Twitter. This referral sharing network can be integrated into the crowdsourcing platform, removing the need for multiple external forums.
- * The quality of referrals can be incentivized in a number of ways. A rudimentary way would be to include a measure of good referrals in the scores used to rank workers and requesters. A separate index could be used for referrals or requesters may reward good referrals with bonus payments.
Benefits of Referral Network
A HIT initially becomes visible to highly rated workers who have worked with that particular requester in the past. The referral network enables sharing of HITs with other (existing and new) workers who would otherwise have been the last in line to see the HIT. This is especially useful when a requester posts a large number of HITs, which can be shared quickly among workers.