Winter Milestone 5 Team Enigma
- 1 System (for task feed and open gov write up)
- 1.1 Brief introduction of the system
- 1.2 How is the system solving critical problems
- 1.3 Introducing modules of the system
- 1.4 Module 1: Estimated Duration of Tasks (Hourly Wage)
- 1.5 Module 2: Task Categories and Ratings
- 1.6 Module 3: Raking the tasks
System (for task feed and open gov write up)
We're going to borrow systems section from this paper as an example: Vaish R, Wyngarden K, Chen J, et al. Twitch crowdsourcing: crowd contributions in short bursts of time. Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014: 3645-3654. Please note how this section was divided into different parts. Please follow the same template.
Brief introduction of the system
There is a massive amount of information necessary for a healthy crowdsourcing marketplace — for example accurate reputation ratings, skill tags on tasks, and hourly wage estimates for tasks — that is privately held by individuals, but rarely shared. We introduce Boomerang, an interactive task feed for a crowdsourcing marketplace, that incentivizes accurate sharing of this information by making the information directly impact their future tasks or workers. Requesters' ratings of workers, and their skill classifications of tasks, are used to give early access to workers who that requester rates highly and who are experts in that skill, so giving a high rating to a mediocre worker dooms the requester to more mediocre work from that worker. Workers' ratings of requesters are used to rank their high-rated requesters at the top of the task feed, and their estimates of active work time are used to estimate their hourly wage on other tasks on the platform.
How is the system solving critical problems
Finding relevant tasks that pay out well and in time is a fundamental need for the workers. Meanwhile, finding skilled workers that deliver good quality results is requesters' aim. Boomerang aims to solve this problem by creating a feedback system that affect the users directly on the system in future. While requester reputation is the major criteria for workers trying to find tasks, there are other preferences and signals that workers look at while trying to find relevant tasks. One such indicator is the hourly wage equivalent that the task would pay. We extend the boomerang idea to include hourly rate as one of the signals for ranking tasks on the task feed. The system works by taking from workers as input, the time it takes them to complete a task. In addition to the workers' and requesters' ratings, we use the hourly wages as a way to bump up the tasks on to the task feed. Another signal for bumping up the task on the feed would be the worker's categorical rating as perceived by the requester. The bumping up of tasks will be valid only for requesters' that are in the same ratings zone, eg:- all requesters that have received tick + ratings only. This system increases interactions between workers and requesters that not only have good reputation but also keep posting tasks that are more aligned with requesters's skills and interests and pay fair wages.
The hourly wage model for the tasks on the feed will be built over the existing task completion times reported by the workers for submitted tasks. The incentive for workers to report accurate time is the availability of hourly wage calculated for other tasks, and effective ranking of tasks on their feed.
Introducing modules of the system
Below, we introduce the various modules of the suggested extension to Boomerang .
Module 1: Estimated Duration of Tasks (Hourly Wage)
The Boomerang model ensures that the problem of reputation inflation is kept in check by incentivizing users to give accurate ratings that directly affect their future activity on the crowdsourcing platform. While the overall experience of the workers with requesters might have been positive, the workers will still be benefited by a more granular preferences for the tasks showing up on their task feed. Workers find it helpful to know how much time a particular task would take before attempting that task. Moreover workers are known to track their hourly wage rates through third party extensions or scripts. The question remains how reliable are these durations reported by other workers on third party forums and platforms. There should be an incentive for the workers to report time taken to complete a task accurately.
Currently workers depend on third party sites to find out estimated time it would take them to finish a task. These reported durations are not always accurate as there is practically no personal incentive for workers to accurately measure and report the same. We let the workers report a completion time for each task they complete. Based on these reported times, we build a model to predict the hourly wage of the individual worker for the tasks that occur on their task feed. This model takes into account the time it took for other workers to complete the same task and the time it took the same worker to complete other tasks.
- Every worker on the system will be asked to provide a input for the amount of time it took them to complete a task while submitting the same. The worker will be provided a stopwatch timer on the task screen to track the time. The timer starts once the task is accepted, and the worker can pause and resume the timer. On clicking on submit, a confirmation with recorded time is displayed to the worker. At this point the worker can either submit the recorded value or can modify the time as he/she sees fit. This also allows the workers to enter time for tasks of longer duration where they might not be on the system the entire time, or took breaks in between etc.
- The system calculates a mean completion time X for every active task on the platform. This is the mean time taken by all the workers who have attempted this task. For new tasks, the mean time is as reported on the prototype tasks phase. If the requester did not opt for a prototype task, we would simply have to wait for the first worker to attempt this task.
- The system also calculates, for every task submitted by the worker, a signed deviation from the mean time X. All such deviations in the same task category are then averaged out to get a single negative/positive value of a mean deviation per category. Let's denote this by D(i) for category i.
- For every task on the worker's feed belonging to a given category i, we use D(i) in a linear regression model to predict the estimated time for the task. We use the simplest model by adding the above mean deviation D(i) to the mean completion time X for a task t. Thus, the Estimated Time for task t is given by ET(t) = X + D(i)
- Hourly wage can simply be calculated by dividing the reward value by the Estimated Time.
Module 2: Task Categories and Ratings
While the boomerang rating captures the overall reputation of the workers and requesters on the platform, certain workers might be exceptional in one category of task than the other. The requester will benefit by making his task available to such workers leading to faster completion of the project. The workers will benefit by having work aligned to their interests up in the task feed. Having category specific ratings is beneficial for decisions.
The system to maintain category ranking is simple and straightforward. Each task created by the requester is assigned an appropriate category. This is done by the requester while authoring the task. The incentive for the requester is better visibility of his task to the workers. The boomerang model asks the user to rate requester on each task. This same rating is added to corresponding category rating for the requester. Categorization of tasks is also helpful for workers to analyze and improve their skills. This categorical rating is further used to rank the tasks better in the task feed. Categories are also displayed as tags next to the tasks and clicking on the same will filter and display all tasks belonging to that category.
Module 3: Raking the tasks
The boomerang ratings provides a holistic way of ranking tasks where all aspects of a given task are assumed to be implied in the ratings provided by the users. This solves the problem of reputation inflation. Extending the same concept, the proposed system aims to make the ranking better by using the previous two modules explained in this paper.
The system improves the ranking of tasks on the worker's feed by taking into account the hourly wages as well as category specific ratings we described in the previous two modules. Along with the ratings of the past, this includes the facts (reward) and predictions (Estimated Time) from the current job to rank the tasks. The improvement over the boomerang's ranking system is to bump up the tasks for which overall ratings fall roughly within the same locality.
The primary key for ranking tasks on the feed is boomerang ratings. The proposed system further ranks these tasks within the local group. The local groups of tasks on the feed are created based on boomerang ratings. The system breaks down the ratings into quartiles or semi-quartiles. The bumping up of tasks is allowed only for tasks belonging to the same rating quartile. Once the first level of ranking is done based on boomerang ratings, the system bumps up the tasks that belong to a category in which the worker has received higher ratings. This will lead to recalculation of the ranks, and for the bumped up tasks the category ratings are considered as they are more relevant for the given task. This is the final ranking of tasks based on any kind of rating. This is the default rank of tasks displayed to the workers.
The per hour wage equivalent for each task is displayed next to the reward awarded by the task. The task feed will have a toggle to enable wage based ranking. On toggling this on, the tasks in the same quartile rating as derived above will now be ranked based on per hour wage. We chose a quartile instead on a global wage ranking because often workers have preferred a reputable and reliable requester who they've worked with before to a possibility of a slightly higher wage from unreliable requesters. Moreover this method of a secondary local ranking maintains the importance of the Boomerang mechanism which is central to our system.