Amazon Mechanical Turk

Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace that connects businesses or individuals, called Requesters, with a large, on-demand, global workforce, called Workers.

In a nutshell, MTurk allows Requesters to easily outsource simple, discrete tasks that require human intelligence but are still difficult for computers to handle—like image tagging or data verification. Workers complete these “Human Intelligence Tasks” (HITs) for a small fee. It’s an efficient way to break down large projects into thousands of tiny, manageable microtasks for a distributed workforce.

Background

The name “Mechanical Turk” actually comes from a historical, 18th-century “automaton” that appeared to play chess on its own, baffling royalty and scholars! The secret? A brilliant human chess master was hidden inside the cabinet, moving the pieces. The machine only looked like it was doing the work.

That’s the core idea of modern MTurk: it provides a digital interface that makes it look like a computer is solving a problem, but in reality, a human is quickly solving the task behind the screen.

The tasks are called Human Intelligence Tasks (HITs) because they require human judgment, which current computers can’t do effectively. Think of a task like: “Draw a box around every traffic light in this photo.” This is easy for a person but very complex for an AI (though AI is getting better!).

Analogy: Imagine a huge pile of paper forms that need the handwritten names transcribed into a spreadsheet. Instead of one person spending weeks on it, you tear the pile into ten thousand tiny strips—each with just one name—and ask a thousand people online to transcribe one strip each. The job is done in an hour! MTurk is the platform that handles all the tearing, distributing, collecting, and paying.

How MTurk Works in Practice

1. The Requester (The Employer):

   • This is the business, researcher, or individual that needs a task completed.

   • They break their big job (like “clean up a database of company names”) into thousands of small HITs (e.g., “Is ‘Acme Corp.’ the same as ‘Acme Corporation’?”).

   • They set the price for each HIT and define the qualifications for the people who can work on it. They are the ones who ultimately approve or reject the work.

2. The Worker (The Crowdworker or ‘Turker’):

   • This is the person who performs the tasks for payment.

   • They browse the list of available HITs, choose the ones they want to do, and complete them online, usually for a few cents or a few dollars per task.

   • They are the “human intelligence” being leveraged.

3. The MTurk Platform (Amazon):

   • This is the marketplace that connects Requesters and Workers.

   • It handles the technical side: posting the HITs, distributing them to the global workforce, collecting the submitted answers, and managing the payment system. Think of it as the digital stage for the entire operation.

Essentially, the Requester posts a problem, the Worker solves it, and MTurk makes the connection seamless.

Use Cases

That’s a fantastic question! You’ve got the concept and the players down, which means you’re ready to see how MTurk is used in the real world. This is where it gets really interesting! 💡

Let’s look at the diverse use cases, focusing on two major categories: Data Processing and Cleaning and Machine Learning Development.

1. Data Processing and Cleaning

Many businesses have large amounts of unstructured, “messy” data that computers can’t handle well. They use MTurk to hire humans to clean it up.

Example Task	What the Worker Does	Why a Computer Can’t Do It Well
Data Verification	Comparing two lists of business names (e.g., from different databases) and verifying which ones are duplicates.	Variations in spelling, abbreviations, and formatting confuse automated systems.
Data Transcription	Transcribing handwritten information from scanned forms or receipts.	Handwriting is highly variable and challenging for Optical Character Recognition (OCR) software.
Information Gathering	Visiting a company’s website to find and log their current store hours or phone number.	This requires navigating a dynamic website, which is easier for a human than a bot.

2. Machine Learning (ML) Development

This is one of the most common and crucial uses of MTurk! To train a Machine Learning model (like one that recognizes objects in photos), you need massive amounts of accurately labeled data.1 That labeling is done by humans on MTurk.2

Example Task	What the Worker Does	Why This is Crucial for AI
Image Labeling	Drawing bounding boxes around specific objects in images (e.g., cars, pedestrians, signs).	The AI needs “ground truth” examples to learn what a car looks like.
Content Moderation	Reviewing user-submitted photos or text and flagging inappropriate or offensive content.	This requires subjective human judgment based on policy and context.
Sentiment Analysis	Reading a tweet or product review and classifying the sentiment as ‘Positive,’ ‘Negative,’ or ‘Neutral.‘	AI can struggle with sarcasm, slang, and subtle human language nuances.

You can see how MTurk essentially plugs human judgment into processes that would otherwise be too time-consuming, expensive, or complex for pure automation.

References

• What is Data Labeling? - Data Labeling Explained - AWS
• Training data labeling using humans with Amazon SageMaker Ground Truth - Amazon SageMaker AI