Many of the most successful and widely used machine-learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three-quarters of their income this way. But even though many work for some of the richest AI labs in the world, they are paid below minimum wage and given no opportunities to develop their skills.
Saiph Savage is the director of the human-computer interaction lab at West Virginia University, where she works on civic technology, focusing on issues such as fighting disinformation and helping gig workers improve their working conditions. This week she gave an invited talk at NeurIPS, one of the world's biggest AI conferences, titled "A future of work for the invisible workers in AI." I talked to Savage on Zoom the day before she gave her talk.
Our conversation has been edited for clarity and length.
You talk about the invisible workers in AI. What sorts of jobs are these people doing?
A lot of tasks involve labeling data—especially image data—that gets fed into supervised machine-learning models so that they understand the world better. Other tasks involve transcribing audio. For instance, when you talk to Amazon's Alexa you might have workers transcribing what you say so that the voice recognition algorithm learns to understand speech better. And I just had a meeting with crowdworkers in rural West Virginia. They get hired by Amazon to read out a lot of dialogue to help Alexa understand how people in that region talk. You can also have workers labeling websites that might be filled with hate speech or pedophilia. This is why, when you search for images on Google or Bing, you're not exposed to those things.
People are hired to do these tasks on platforms like Amazon Mechanical Turk. Large tech companies might use in-house versions—Facebook and Microsoft have their own, for instance. The difference with Amazon Mechanical Turk is that anyone can use it. Researchers and startups can plug into the platform and power themselves with invisible workers.
From MIT Technology Review
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