Shortcomings of Amazon Mechanical Turk May Threaten Natural Language Generation
unite.ai
unite.ai
CrowdFlower maintained statistics about the accuracy of individual workers. Additionally, they made it easy to include gold-standard "test" questions, which weeds out workers who are not doing the specific task correctly.
Without this sort of quality-control platform, mechanical turk is just unreliable crowdsourced-labor infrastructure. I understand that AWS doesn't want to build too much on the services they provide but, really, mechanical turk sucks.
I'm working with a researcher who is conducting experiments using turk, and he has a half-written shoddy version of what crowdflower offered. It seems that everyone using turk has to reinvent the wheel. Why can't someone provide decent quality control over turk results?
Quality/metrics layer as a Saas with MT on backend, add a few percent to the price per work unit?
I know Stanford's research teams all use a common interface to mturk that keeps profiles of turkers on their side so they know who to solicit for upcoming surveys, conduct longitudinal studies, etc. I've always wondered why more universities didn't follow suit.
I built a side project called cogmint based on the insight that simple scoring and ranking of workers was valuable. I ended up building my own worker interface instead of using mturk because it wasn't much additional effort on top of the scoring logic I was building anyway. Perhaps other serious companies came to the same conclusion I did with my hobby project.
Are they 1:1 your peers, colleagues?
Or are they underpaid, most likely emerging economy countries, and very overworked labor that you're paying for?
You can browse to the mechanical turk subreddits and you'll hear the other side of the table that they spend 15-20-hours on a task to get what, a few dollars?
Pay more, get better results. The system itself is designed to exploit.
I created Cogmint.com ("cognition minting") to solve this problem for myself.
You can submit known correct answers for questions, and those questions are then used as ground truth to score worker accuracy. Workers are then scored on their similarity to known correct answers and other workers that have accurately answered questions. It works surprisingly well for how simple it is. It's been a fun challenge to create simple methods of scoring similarity across different task types.
It's a side project, so don't rely on it for mission critical things, but I rely on it for some production tasks, so it's stable.
It currently supports classification (choose from a set of possible answers) and has beta support for bounding box task types. String input task types are coming very soon.
I'd love to see if it can help you out, I'll waive the fees: I'm not in it for the money I just like making things useful and reliable. Reach out and say hi!
My undergrad thesis was to build https://tagbull.com, where we tried to have turkers validate the work of other turkers by breaking up a label into sub tasks, and getting multi-turker consensus on those before moving forward.
The main issue we ran into is that the incentive system is incredibly misaligned with the responsibility that the turkers have. It’s very difficult to build trust, especially with a crowd of people who haven’t signed contracts, and who face virtually no repercussions for doing bad work, whether intentionally or unintentionally.
Have you noticed problems that show up with questions whose answers have a bimodal distribution (ie. The gold standard question actually has two or more correct answers)?
In one sense, this is just a labeling quality problem with the 'gold standard' data, but to a lesser extent these same issues may crop up in the data being labeled when using similarity or clustering to rate or classify the workers and transitively apply that to the other results they produce.
I haven't dug into the data on this across the platform but you've given me the idea to go see if I can find evidence of this, and see if I can improve somehow. There's only low hundreds of projects, so I might be able to find some that have this problem.
For doing research, I think Prolific [1] is a popular alternative.
They include features like pre-screening based on demographic features (including participants' approval rates in past studies) [2], and attention check questions [3].
[2]: https://researcher-help.prolific.co/hc/en-gb/articles/360009...
[3]: https://researcher-help.prolific.co/hc/en-gb/articles/360009...
We're happy to have a conversation - feel free to reach out to me via my profile email or you can find our contact details at https://www.tasq.ai/
[1] - https://www.newscientist.com/article/2176436-bots-on-amazons...
If higher prices were paid across the board, doubtlessly more workers with the sought after skills would sign up though. So maybe that was the thrust of your comment.
This is plausible but doesn't change the basic problem. Of course, workers on AMT have years of experience trying to game it. Just more pay isn't going to create a sudden sense of solidarity with the employer.
I would guess that quality results at scale would require a system built from the ground-up to cultivate some degree of loyalty or something.
It's kind of a dilema of capitalism itself.
At scale, you can pay people to accomplish a task for peanuts but they lack loyalty and will cut-corners whenever it's possible and convenient. And that still gets a whole lot of things done for cheap in this world.
But if you want to get loyalty, people you don't have to watch, etc., you have to organize an entire enterprise for that, maybe show loyalty yourself (Turkers are inherently throw-away, why should they be loyal for just today's wages). And the cost of that arrangement tends to multiply.
Maybe a couple? But you're also going to get millions more unskilled workers. Without good filtering, no pay level is going to work.
Interesting. When I go to login to Amazon with a VPN it puts me in a CAPTCHA loop and never lets me access my account. I have to login with a residential IP address to access my account.
Anyway, I don’t like that service either.
edit: I see that wearywanderer explained what I mean as a sibling, that’s why I think GP didn’t get precisely what the MT was and how it was operated
Do you have a source for that assertion? As far as I can tell, several of the chess players known to have operated the Turk had substance abuse (chiefly alcoholism), health, and money problems (the words 'debt', 'penniless', and 'destitute' come up a few times). While not proof of abuse, it does suggest a strategy of recruiting the vulnerable.
I understand what you mean if we judge it from modern western standards, but I don’t agree if we judge it by the standards of that era.
It’s all a matter of context IMHO.
The name was meant to inspire dread in those playing against it, much like how you know shit has gotten real when you have to fight robot Hitler in an American game.
They even say it on the site: 'Artificial Artificial Intelligence'
You're technically not wrong, but from your tone and wording I think you have a misconception about the machine, or are trying to induce such a misconception in others, to inspire others to take offense at the premise. There was never a Turkish person hidden inside the Mechanical Turk. The "foreigners" inside were a variety of chess masters from Germany, Austrian, French and the UK. Not the oppressed immigrants a modern reader might imagine when speaking of Turks and unspecified foreigners. And I suspect the chessmaster operators of the original Mechanical Turk were not remotely representative of the demographics of modern Amazon MT users. A Frenchman participating in a scheme to bamboozle some Austrian princes is not exactly something worth getting bent out of shape over.
(Furthermore I think in at least some of the cases, the chessmasters were operating the machine in their home country and weren't foreigners at all.)