9 ms·

The ‘invisible’, often unhappy workforce that’s deciding the future of AI

110 points by Hard_Space 5y ago

PaulHoule 5y ago
When you use Turks the toughest problem is managing the workers who work at a high rate but do poor quality work. Some of these people might do better work if they got feedback (e.g. would be willing to pay them a bonus if they slow down and do a better job. It’s tempting though just to cut them off.
raxxorrax 5y ago
You could just stop all the crap you are doing and believe what you want. If prejudice is already in the question, you cannot gain "neutral" answers. Or nuanced for that matter... I personally think that the fight against hate speech in the name of minorities is far more offensive than a visit to the darkest corners of 4chan. Perhaps objectively so, since the latter group is more diverse (international audience) and inclusive (low standards) than contemporary academia, which proposes these metrics as a gold standard. But the technical challenge is not solvable as soon as there are different standards of allowed content. Everybody has to decide those limits for himself, parents in case of minors. At least that is true for universal platforms. Some people live up when they get headwind, some people might feel inhibited. The quest against hate speech is intuitive one but still very foolish. In my experience is that people search out offensive material to be offended. There are cases of harassment that are separate from this and often people seem to misjudge this. But AI is far away from making competent choices here. An annotator service like suggested here would mainly be a service to select the sample group to spit out predetermined results. Maybe that is a service Google like to provide. They are also just people pleaser I guess. You could strip people of their anonymity and they will get happier in polls, more content with management. How would you evaluate the change in the data you see?
- dr-detroit 5y ago
  Those international flags on 4chan are put there by shills to make you feel good about chatting with Europeans but behind the scenes its all internet research agency scumbags sitting in a call center at 55 savushinka, Moscow. US Congress has released mountains of information on this subject. You are trapped in a cult!
- beepbooptheory 5y ago
  I'm not really sure what you are angry about here because you dont seem to be arguing against the main conceit that bias of whatever form is a factor in constructing these data sets. Is it just that, for you, bias is inevitable and we simply have to toughen up?
  - raxxorrax 5y ago
    In general I am angry about censorship ambitions and futile and mislead attempt to get rid of hate by banning it from the internet. Even while I am aware that hate can reproduce in a simple scheme that can lead to mutual radicalization, previous attempt to contain it all made the situation worse. But correction is nowhere in sight, it is as if people try to implement insanity by committing to the same mistakes over and over. But I am not that angry and I don't think that can be read out of my comment aside from general disapproval. I would assume that gig workers did not care as much about hate speech as some academics do and did not flag content as expected. This discrepancy is declared as bias. Fine, be that way... > The Google researchers suggest that ‘[the] disagreements between annotators may embed valuable nuances about the task’. On that I agree with the researchers, but would propose that any annotation (hate speech yes/no) would have to fall back to the 'no' and solve the dispute. Otherwise only asking the target will provide any additional understanding. Perhaps asking as supreme court too, but that is not feasible and not even the highest courts are infallible.
- mistrial9 5y ago
  strongly worded here; insensitivity ("I don't care about you") and disregard ("not in your shoes, don't care") are fertile ground for casual offensive speech and no, it is not technologies job to fix that .. BUT in the real world there are a series of feedback loops, from discomfort, to yelling, to a slap in the face or an arrest by an officer. This works in the real world, where is the feedback in a digital world? The cue here for me was "You could strip people of their anonymity and they will get happier in polls" as an unexamined backing into the feedback topic.. real people have feedback that sticks What "feedback that sticks" is natural in digital realms, and how can it evolve, not dictatorially?
- JaimeThompson 5y ago
  "In my experience is that people search out offensive material to be offended." Lots more, it seems, seek out people to try to deliberately offend / intimidate others.
MomoXenosaga 5y ago
The gig economy doesn't lead to happy employees- truly shocking. Who wouldn't want to dedicate their body and soul to a silicon valley corporation?! To be a replaceable cog in the machine is the American dream.
- spaetzleesser 5y ago
  “Who wouldn't want to dedicate their body and soul to a silicon valley corporation?” A lot of people here do that but at least they make good money doing it. Gig workers don’t get the money.
- oneoff786 5y ago
  The funny thing about the American dream is that the quintessential examples used to be blue collar or back office labor jobs like mailroom processors could work hard and rise to the top. Kind of a pipe dream but it did happen. Even if you believe that to still be true, gig workers start even lower, below the bottom rung of the ladder. They’re contractors doing spot jobs in mailrooms. Most janitor jobs are gone. They’re now contractors too. Healthcare is to blame for a lot of this. The cost of a low level employee is simply too high when companies need to cover the healthcare coverage costs.
  - onemoresoop 5y ago
    > could work hard and rise to the top What are the odds of that?
    - cbozeman 5y ago
      Not good, because getting to the top of any enterprise is inherently competitive. Everyone wants the job, and there's only one of them to go around. This is the reason you see such similar personalities occupying extremely high positions in companies. You need a certain mentality and dedication to set everything to the wayside for the pursuit of climbing the corporate hierarchy.
      - 5y ago
b409ba0801cd21 5y ago
I wonder if there have been any efforts to sabotage crowdsourced AI training and content moderation by signing up on crowdworking platforms and intentionally providing false responses. A large and tech savvy enough sabotage ring could use a browser extension or the like to keep their responses straight and increase the odds of their fake answers being accepted.
- erehweb 5y ago
  Seems like a lot of tedious work at low pay for little impact, particularly for people who are tech savvy enough.
- renewiltord 5y ago
  It was tried. 4chan tried a coordinated “penis” prank on Recaptcha. Despite the much vaunted community power of the website, and despite being coordinated, nothing happened. It turns out that they are a drop in the bucket. Not only is there low RoI but also the group is too weak.
- gruez 5y ago
  like this? https://i.imgur.com/aU21k.jpg https://i.imgur.com/aU21k.jpg
  - Aulig 5y ago
    Did anything ever come of that? Because nowadays, those captchas are no longer used.
- theamk 5y ago
  It is pretty common to "verify" workers: a fraction of questions (often 1% to 10%) is asked-before questions with known correct answers. If those are not answered correctly, the entire dataset from this person is ignored. Depending on the platform, they might get paid less as well. This is designed to detect workers who either did not understand the instructions, or those who don't care about those and answer randomly. But this works against intentional sabotage as well.
- 5y ago
SMAAART 5y ago
Ground truth basic dynamics are similar to the basic dynamics of Prediction Markets. Having experimented with creating Prediction Markets from scratch I have witnessed first hand how the bias of the participants will skew or even nullify the wisdom of the crowd, with wisdom-free results. > ‘[A] large majority of crowdworkers (94%) have had work that was rejected or for which they were not paid. Yet, requesters retain full rights over the data they receive regardless of whether they accept or reject it; Roberts (2016) describes this system as one that “enables wage theft”. So, what was the source of wisdom in rejecting the results?
- visarga 5y ago
  > So, what was the source of wisdom in rejecting the results? You can run the same task 3 or 5 times and take the consensus option rejecting the others. But from experience unless the taggers have had special training in the current task the quality of annotations is crap. Then you have to do it all over again with someone in-house and gain nothing from the whole crowd sourcing ordeal. Outside people tend to think anyone could label examples, but practice shows that it takes a special kind of person to do it well. Probably this is why many get their work rejected.
  - SQueeeeeL 5y ago
    >Probably this is why many get their work rejected. It's interesting we've built a system where people are allowed to be given a job and then told they're unqualified to perform it. If a construction site hired someone as a heavy machine operator and then immediately decided to fire them, they'd still be owed some form of wages.
    - bjornsing 5y ago
      The owed wages wouldn’t be the major problem though…
      - SQueeeeeL 5y ago
        A days work at least, definitely isn't free
deleted 5y ago
[deleted]
RosanaAnaDana 5y ago
Its worth hiring people full time to do high quality labeling. Benefits, 401k, the whole 9. A good technician who can interpret nuance has value.
- hooande 5y ago
  It's just not as efficient because the size of a dataset is often the biggest factor in determining performance. simply, I get way more labels if I'm paying $0.10 each vs the cost of a full time salary. And individuals are prone to burnout with repetitive tasks Data labeling is one job that seems to lend itself well to gig work. There were many times where I would have gladly spent a day labeling images in exchange for $50, no need for benefits
  - RosanaAnaDana 5y ago
    >It's just not as efficient because the size of a dataset is often the biggest factor in determining performance. I fundamentally disagree. Its not the size of the dateset that determines performance but the quality. Sure. Bigger is better. But simply better is *way* better. I can do more with 1000 high quality examples of the thing than I can with 40k low quality ones. I've gone as far as bringing on 40 digitizers at once full time. I would never have been able to accomplish that project with turks. I would never have been able to call a team meeting and explain a very specific shift in interpretation we would be doing regarding the relationship of feature apple to feature banana, and when and where it would apply. The difference between thinking you need oom more examples over a few quality ones I think depends on how in the thick of it you've been with regards to ML on theoretical grounds versus doing the boots on the ground work to get a product out. Having control over digitization and labeling and being willing to pay for high quality label's has allowed me to get results that directly meet my clients needs as opposed to sitting in some uncanny valley where the results are close, but no cigar.
    - CardenB 5y ago
      The recent trend of self supervision in computer vision disagrees with this hypothesis.
andreyk 5y ago
Not a very novel point - The Ghost Workers Powering The AI Economy https://www.forbes.com/sites/adigaskell/2019/09/02/the-ghost-workers-powering-the-ai-economy/?sh=29209db043eb https://www.forbes.com/sites/adigaskell/2019/09/02/the-ghost... AI needs to face up to its invisible-worker problem https://www.technologyreview.com/2020/12/11/1014081/ai-machine-learning-crowd-gig-worker-problem-amazon-mechanical-turk/ https://www.technologyreview.com/2020/12/11/1014081/ai-machi... That being said, this one covers two recent research works that are quite interesting (Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation , The Origin and Value of Disagreement Among Data Labelers: A Case Study of Individual Differences in Hate Speech Annotation)
bserge 5y ago
Lol, that's great, the working class will get their revenge one way or another.
d--b 5y ago
Haha, and this is how the singularity is going to turn out socialist. Mike Judge should make a movie about this.
CarrieLab 5y ago
I'm on the team at Surge AI (data labeling platform + workforce), so this article hits home. We started Surge AI precisely because our team has always been against the adversarial, penny labor design of crowdwork systems: systems dependent on multi-annotator consensus lead to poor quality data (and ignore the inherent subjectivity in the rich, language-based tasks I love), they lead to suboptimal outcomes and treatment for all parties, and you get get what you pay for! For example: 1. Most data labeling systems don't allow you to communicate meaningfully with your workforce. In contrast, we prize two-way communication; your data labelers are the ones going through tens of thousands examples, so they often have amazing feedback for how to improve your data and design your tasks better. And of course, you often have questions for them as well. 2. Context matters. I can't label Spanish hate speech; someone from Mexico City often can't label Madrid slang either. 3. The majority vote isn't always the best one. Real-world data is often personalized and subjective; your opinion on a funny or angry story may not match mine, and that's okay. Our training sets and AI models should reflect that. We just wrote a blog post on the subtle nuances when considering majority votes and inter-rater reliability metrics: https://www.surgehq.ai/blog/the-pitfalls-of-inter-rater-reliability-in-data-labeling-and-machine-learning https://www.surgehq.ai/blog/the-pitfalls-of-inter-rater-reli... 4. Curating annotator pools. Our product is designed around helping you build custom labeling teams that you trust, who learn the nuances of your domain and stay with you over time.
- deleted 5y ago
  [deleted]
stevenicr 5y ago
Seeing the headline made me remember that I wondered similar about google's hiring from another country to be quality guidelines raters or something like that. I'd like to know more about the people and the culture there - not because it's easy to miss some culture thing here- but not knowing small things, the quality raters could be pushing some type of content into X category and other things into Y - and not because it's in the tons of pages of text they are supposed to follow, but because - whatever religion / other culture things are a thing there.

110 points by Hard_Space 5y ago

When you use Turks the toughest problem is managing the workers who work at a high rate but do poor quality work. Some of these people might do better work if they got feedback (e.g. would be willing to pay them a bonus if they slow down and do a better job. It’s tempting though just to cut them off.

raxxorrax 5y ago

You could just stop all the crap you are doing and believe what you want. If prejudice is already in the question, you cannot gain "neutral" answers. Or nuanced for that matter... I personally think that the fight against hate speech in the name of minorities is far more offensive than a visit to the darkest corners of 4chan. Perhaps objectively so, since the latter group is more diverse (international audience) and inclusive (low standards) than contemporary academia, which proposes these metrics as a gold standard. But the technical challenge is not solvable as soon as there are different standards of allowed content. Everybody has to decide those limits for himself, parents in case of minors. At least that is true for universal platforms. Some people live up when they get headwind, some people might feel inhibited. The quest against hate speech is intuitive one but still very foolish. In my experience is that people search out offensive material to be offended. There are cases of harassment that are separate from this and often people seem to misjudge this. But AI is far away from making competent choices here. An annotator service like suggested here would mainly be a service to select the sample group to spit out predetermined results. Maybe that is a service Google like to provide. They are also just people pleaser I guess. You could strip people of their anonymity and they will get happier in polls, more content with management. How would you evaluate the change in the data you see?

dr-detroit 5y ago
Those international flags on 4chan are put there by shills to make you feel good about chatting with Europeans but behind the scenes its all internet research agency scumbags sitting in a call center at 55 savushinka, Moscow. US Congress has released mountains of information on this subject. You are trapped in a cult!
beepbooptheory 5y ago
I'm not really sure what you are angry about here because you dont seem to be arguing against the main conceit that bias of whatever form is a factor in constructing these data sets. Is it just that, for you, bias is inevitable and we simply have to toughen up?
- raxxorrax 5y ago
  In general I am angry about censorship ambitions and futile and mislead attempt to get rid of hate by banning it from the internet. Even while I am aware that hate can reproduce in a simple scheme that can lead to mutual radicalization, previous attempt to contain it all made the situation worse. But correction is nowhere in sight, it is as if people try to implement insanity by committing to the same mistakes over and over. But I am not that angry and I don't think that can be read out of my comment aside from general disapproval. I would assume that gig workers did not care as much about hate speech as some academics do and did not flag content as expected. This discrepancy is declared as bias. Fine, be that way... > The Google researchers suggest that ‘[the] disagreements between annotators may embed valuable nuances about the task’. On that I agree with the researchers, but would propose that any annotation (hate speech yes/no) would have to fall back to the 'no' and solve the dispute. Otherwise only asking the target will provide any additional understanding. Perhaps asking as supreme court too, but that is not feasible and not even the highest courts are infallible.
mistrial9 5y ago
strongly worded here; insensitivity ("I don't care about you") and disregard ("not in your shoes, don't care") are fertile ground for casual offensive speech and no, it is not technologies job to fix that .. BUT in the real world there are a series of feedback loops, from discomfort, to yelling, to a slap in the face or an arrest by an officer. This works in the real world, where is the feedback in a digital world? The cue here for me was "You could strip people of their anonymity and they will get happier in polls" as an unexamined backing into the feedback topic.. real people have feedback that sticks What "feedback that sticks" is natural in digital realms, and how can it evolve, not dictatorially?
JaimeThompson 5y ago
"In my experience is that people search out offensive material to be offended." Lots more, it seems, seek out people to try to deliberately offend / intimidate others.

MomoXenosaga 5y ago

The gig economy doesn't lead to happy employees- truly shocking. Who wouldn't want to dedicate their body and soul to a silicon valley corporation?! To be a replaceable cog in the machine is the American dream.

spaetzleesser 5y ago
“Who wouldn't want to dedicate their body and soul to a silicon valley corporation?” A lot of people here do that but at least they make good money doing it. Gig workers don’t get the money.
oneoff786 5y ago
The funny thing about the American dream is that the quintessential examples used to be blue collar or back office labor jobs like mailroom processors could work hard and rise to the top. Kind of a pipe dream but it did happen. Even if you believe that to still be true, gig workers start even lower, below the bottom rung of the ladder. They’re contractors doing spot jobs in mailrooms. Most janitor jobs are gone. They’re now contractors too. Healthcare is to blame for a lot of this. The cost of a low level employee is simply too high when companies need to cover the healthcare coverage costs.
- onemoresoop 5y ago
  > could work hard and rise to the top What are the odds of that?
  - cbozeman 5y ago
    Not good, because getting to the top of any enterprise is inherently competitive. Everyone wants the job, and there's only one of them to go around. This is the reason you see such similar personalities occupying extremely high positions in companies. You need a certain mentality and dedication to set everything to the wayside for the pursuit of climbing the corporate hierarchy.
    - 5y ago

b409ba0801cd21 5y ago

I wonder if there have been any efforts to sabotage crowdsourced AI training and content moderation by signing up on crowdworking platforms and intentionally providing false responses. A large and tech savvy enough sabotage ring could use a browser extension or the like to keep their responses straight and increase the odds of their fake answers being accepted.

erehweb 5y ago
Seems like a lot of tedious work at low pay for little impact, particularly for people who are tech savvy enough.
renewiltord 5y ago
It was tried. 4chan tried a coordinated “penis” prank on Recaptcha. Despite the much vaunted community power of the website, and despite being coordinated, nothing happened. It turns out that they are a drop in the bucket. Not only is there low RoI but also the group is too weak.
gruez 5y ago
like this? https://i.imgur.com/aU21k.jpg https://i.imgur.com/aU21k.jpg
- Aulig 5y ago
  Did anything ever come of that? Because nowadays, those captchas are no longer used.
theamk 5y ago
It is pretty common to "verify" workers: a fraction of questions (often 1% to 10%) is asked-before questions with known correct answers. If those are not answered correctly, the entire dataset from this person is ignored. Depending on the platform, they might get paid less as well. This is designed to detect workers who either did not understand the instructions, or those who don't care about those and answer randomly. But this works against intentional sabotage as well.
5y ago

SMAAART 5y ago

Ground truth basic dynamics are similar to the basic dynamics of Prediction Markets. Having experimented with creating Prediction Markets from scratch I have witnessed first hand how the bias of the participants will skew or even nullify the wisdom of the crowd, with wisdom-free results. > ‘[A] large majority of crowdworkers (94%) have had work that was rejected or for which they were not paid. Yet, requesters retain full rights over the data they receive regardless of whether they accept or reject it; Roberts (2016) describes this system as one that “enables wage theft”. So, what was the source of wisdom in rejecting the results?

visarga 5y ago
> So, what was the source of wisdom in rejecting the results? You can run the same task 3 or 5 times and take the consensus option rejecting the others. But from experience unless the taggers have had special training in the current task the quality of annotations is crap. Then you have to do it all over again with someone in-house and gain nothing from the whole crowd sourcing ordeal. Outside people tend to think anyone could label examples, but practice shows that it takes a special kind of person to do it well. Probably this is why many get their work rejected.
- SQueeeeeL 5y ago
  >Probably this is why many get their work rejected. It's interesting we've built a system where people are allowed to be given a job and then told they're unqualified to perform it. If a construction site hired someone as a heavy machine operator and then immediately decided to fire them, they'd still be owed some form of wages.
  - bjornsing 5y ago
    The owed wages wouldn’t be the major problem though…
    - SQueeeeeL 5y ago
      A days work at least, definitely isn't free

deleted 5y ago

[deleted]

RosanaAnaDana 5y ago

Its worth hiring people full time to do high quality labeling. Benefits, 401k, the whole 9. A good technician who can interpret nuance has value.

hooande 5y ago
It's just not as efficient because the size of a dataset is often the biggest factor in determining performance. simply, I get way more labels if I'm paying $0.10 each vs the cost of a full time salary. And individuals are prone to burnout with repetitive tasks Data labeling is one job that seems to lend itself well to gig work. There were many times where I would have gladly spent a day labeling images in exchange for $50, no need for benefits
- RosanaAnaDana 5y ago
  >It's just not as efficient because the size of a dataset is often the biggest factor in determining performance. I fundamentally disagree. Its not the size of the dateset that determines performance but the quality. Sure. Bigger is better. But simply better is *way* better. I can do more with 1000 high quality examples of the thing than I can with 40k low quality ones. I've gone as far as bringing on 40 digitizers at once full time. I would never have been able to accomplish that project with turks. I would never have been able to call a team meeting and explain a very specific shift in interpretation we would be doing regarding the relationship of feature apple to feature banana, and when and where it would apply. The difference between thinking you need oom more examples over a few quality ones I think depends on how in the thick of it you've been with regards to ML on theoretical grounds versus doing the boots on the ground work to get a product out. Having control over digitization and labeling and being willing to pay for high quality label's has allowed me to get results that directly meet my clients needs as opposed to sitting in some uncanny valley where the results are close, but no cigar.
  - CardenB 5y ago
    The recent trend of self supervision in computer vision disagrees with this hypothesis.

andreyk 5y ago

Not a very novel point - The Ghost Workers Powering The AI Economy https://www.forbes.com/sites/adigaskell/2019/09/02/the-ghost-workers-powering-the-ai-economy/?sh=29209db043eb https://www.forbes.com/sites/adigaskell/2019/09/02/the-ghost... AI needs to face up to its invisible-worker problem https://www.technologyreview.com/2020/12/11/1014081/ai-machine-learning-crowd-gig-worker-problem-amazon-mechanical-turk/ https://www.technologyreview.com/2020/12/11/1014081/ai-machi... That being said, this one covers two recent research works that are quite interesting (Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation , The Origin and Value of Disagreement Among Data Labelers: A Case Study of Individual Differences in Hate Speech Annotation)

bserge 5y ago

Lol, that's great, the working class will get their revenge one way or another.

d--b 5y ago

Haha, and this is how the singularity is going to turn out socialist. Mike Judge should make a movie about this.

CarrieLab 5y ago

I'm on the team at Surge AI (data labeling platform + workforce), so this article hits home. We started Surge AI precisely because our team has always been against the adversarial, penny labor design of crowdwork systems: systems dependent on multi-annotator consensus lead to poor quality data (and ignore the inherent subjectivity in the rich, language-based tasks I love), they lead to suboptimal outcomes and treatment for all parties, and you get get what you pay for! For example: 1. Most data labeling systems don't allow you to communicate meaningfully with your workforce. In contrast, we prize two-way communication; your data labelers are the ones going through tens of thousands examples, so they often have amazing feedback for how to improve your data and design your tasks better. And of course, you often have questions for them as well. 2. Context matters. I can't label Spanish hate speech; someone from Mexico City often can't label Madrid slang either. 3. The majority vote isn't always the best one. Real-world data is often personalized and subjective; your opinion on a funny or angry story may not match mine, and that's okay. Our training sets and AI models should reflect that. We just wrote a blog post on the subtle nuances when considering majority votes and inter-rater reliability metrics: https://www.surgehq.ai/blog/the-pitfalls-of-inter-rater-reliability-in-data-labeling-and-machine-learning https://www.surgehq.ai/blog/the-pitfalls-of-inter-rater-reli... 4. Curating annotator pools. Our product is designed around helping you build custom labeling teams that you trust, who learn the nuances of your domain and stay with you over time.

deleted 5y ago
[deleted]

stevenicr 5y ago

Seeing the headline made me remember that I wondered similar about google's hiring from another country to be quality guidelines raters or something like that. I'd like to know more about the people and the culture there - not because it's easy to miss some culture thing here- but not knowing small things, the quality raters could be pushing some type of content into X category and other things into Y - and not because it's in the tons of pages of text they are supposed to follow, but because - whatever religion / other culture things are a thing there.