Winter Milestone 7
Due date (PST): 8:00 pm 28th Feb 2016 for submission, 12 pm 29th Feb 2016 for peer-evaluation.
This week, we will refine methods and systems proposed last week further:
We have 3 goals for this week:
- Refining our system proposal. Please make comments directly on the google doc. We want to address any holes in our incentive compatible structure and make any concrete decisions regarding things like the # of workers that report time spent on a task before we start displaying it on the taskfeed.
- Design tweaks in order to capture and display the new information we need. This includes a task element to record the time a worker spent and small modifications to the taskfeed to display the rejection rate and effective wage. Please post low-fi or high-fi mocks to the #taskfeed channel.
- Engineering plan of action and division of work. Led by @dmorina.
Michael's summary from last week's hangout:
We have aligned on a specific vision for a smarter, more informative task feed by incentivizing workers and requesters to share information they might not otherwise share, or not share accurately. There are three main components to this:
- Reputation. It shows more accurate reputation information, by influencing which workers get your future tasks (if you're a requester), and which requesters show up at the top of your feed (if you're a worker). The individual incentive: you get better workers, or better requesters, by reporting honestly. The global win: the reputation scores more directly reflect individual incentives. This is Boomerang as previously described.
- Hourly rate. The goal is to for the task feed to show an estimate of how much you'd make with each task (e.g., $9/hr). To do so, it asks workers who just completed the task to estimate how long it took them to do it. The individual incentive: it uses the worker's responses to build a model to estimate their effective hourly rate for all the other tasks in the marketplace. The global win: those workers' responses are used to produce estimates shown to all other workers.
- Rejection information. It shows the % of tasks for workers like you that get rejected, by influencing which workers get the requesters' future tasks based on rejection information. The individual incentive: the more of a worker's tasks a requester accepts, the earlier they get access to their future tasks. (This is a smaller effect than the reputation feedback above, but does have an impact.) This prevents "accept all submissions" degenerate behavior. The global win: workers can now see the % of tasks accepted for workers like them.
Boomerang: Incentivizing Information Disclosure in Paid Crowdsourcing Platforms
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.
The task feed hangouts from last week:
- Youtube link of the task feed meeting 1: watch
- Youtube link of the task feed meeting 2: watch
- Youtube link of the task feed meeting 3: watch
Michael's synthesized needs:
- to find new tasks that will maximize income (reduce uncertainty in payment, rejection, maximize certainty in what will be asked of me and how quickly I can do it)
- to find new tasks that fit my expertise profile
- to refind old requesters' new tasks, since I know I like them
- to identify tasks I can do on my own time
- to learn new skills
Our goal for this week is to run experiments, then analyze the findings and write up a report. By the end of the week, we want to produce a report with findings from our pilot experiments - this will help us in making future decisions.
First half of the week (Volunteer to be a requester): Pilot participants should try to finish their HTML task designs, that takes about an hour. Please message @catherine.mullings for that. Once we have your task designs, @dmorina will post them on Mechanical Turk for you. The taskauthoring folks are looking for a couple volunteers to create some tasks for three datasets we have. We give you the task and some example input/outputs, and you try to write a task interface in Mechanical Turk that will get workers to produce the right answers.
Second half of the week (analyze+report): We analyze the results from the experiment, conduct a Google hangout with @michaelbernstein, and produce a report on the findings. We can start making contributions to the report here on Google doc.
Open Gov and Design
Check out this week's meeting, and based on the open gov discussions here - create a mock, minimal design within Daemo. Think about questions like: how does it work as a system? how it would fit in Daemo? Like, walk us through. I’m a new worker on Daemo. What do I do? Am I already part of a guild? How do I get into one? How do I get work once I’m in one? What if the requester doesn’t like what I do? And how does all this solve the reputation problem? Try to pitch a story/wireframe with specific design that has as few moving parts as possible.
You can use balsamic or Google slides to give shape to your ideas. Design folks, come join and help move this effort forward. Once you're done, post here: http://crowdresearch.meteor.com/category/open-gov
This weeks main issues: #77 (this is a pretty big one), #660, #509
announce in #research-engineering that you are working on a particular issue and please let the others know about the progress of the issues you are working on (so that we don't do duplicate work). You are encouraged to work together.
For any questions ping @aginzberg, @dmorina, and @shirish.goyal on Slack #research-engineering