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May 30, 2022

Best data crowdsourcing platforms in 2024

May 30, 2022

Best data crowdsourcing platforms in 2024

What are Microtasks?

A microtask is a small job that may not require many skills. A microtask normally requires just a low level of skill and takes less time to complete. Microwork is any microtask that has been outsourced to a decentralized, online workforce. Just as the name suggests, the remuneration for completing microtask is also very minimal. With microwork/ microtask, interested companies that require assistance in one way or the other put online tasks on a website and offer compensation for the individual that works and completes the task. Microwork can often be found on specific sites such as Amazon's Mechanical Turk (MTurk), that specialize in connecting remote workers with businesses that have need of short-term freelance workers.

Again, Micro tasking involves the process of dividing large tasks into smaller bits. These tasks are then distributed to interested people on the internet who work remotely and deliver the task after it is completed.  Some of these microtasks include annotations, polygon annotations, identifying the contents of an image or recognizing abstract text.

What are Microtasks Management Platforms?

Microtask management platforms are platforms that handle projects and reduce them to a series of small, manageable, and well-defined tasks that are distributed to remote workers globally.

Generally, microtask platforms are used for small, well-defined, monotonous tasks that usually do not require so many skills. Micro tasking platforms provide a way for anyone sitting at their computer to contribute to these tasks.

What are the Benefits of Microwork?

1. Ensures a more egalitarian labor market.

Advocates of microwork highlight that; microwork makes the labor market more egalitarian. Such advocates believe that with the invention of microwork, all kinds of people, regardless of their race, gender, socio-economic status, or religious background are all equal and deserve equal rights and opportunities.

2. Easy distribution of tasks/ workload

Due to micro work, the workload can evenly be shared amongst people who may need extra income the most. Hence, the workload does not become the burden of only a few persons, rather, it can be shared evenly amongst several people. It gives those who may not have sufficient income access to income so that they can cater for themselves and their families.

3. Gives people the opportunity to earn money for completing a task such as transcribing a video.

4. Easy access to money to support themselves and their family.

Microwork helps remote workers to earn money for their families and attempt to provide them with food but also pay for an education. With the invention of microwork, things have truly changed.

gives people the opportunity to earn money for completing a task such as transcribing a video. People can earn money for their families and attempt to provide them with food but also pay for an education.

 5. Allows workers to work remotely from any part of the world.

Some pros of micro workers include the ability to work from home and the ability to earn extra money besides regular jobs. The ability to be able to work from home normally is of great relevance to women.

6. For employers, microtasking services provide a platform to quickly get a project online and start receiving results concurrently from many workers.

Criticisms of Microwork

1. Language Barrier

It has been argued that many microwork job sites are only in English. So, whoever is unable to communicate and understand the English Language is automatically disqualified from this kind of work. In this situation, language becomes a barrier to performing most micro tasks.

2. Different pay scales

Another major con of micro tasks is the differentials in the pay scales. Micro workers in the job site's home country are likely to be paid more than international micro workers and this fair wage must be equal for similar tasks. This often makes international micro workers often feel underappreciated and underpaid.

 3. Different payment systems

Many micro workers in developing countries may or may not have bank accounts. With those that have the bank accounts, the bank tariffs or electronic levy on international deposits is normally on the high side and this takes away a chunk of their money earned.

Examples of Microtask jobs

1. Tagging Photos from social media

2. Digitizing Receipts & Invoices

3. Typing out business card information from a photo

4. Labeling photos

5. Crowdsourcing

6. Describing products

7. Transcribing scanned documents

Uses of Microtasks

1. Involves processing online data. E.g., Driving traffic to websites, labeling, or tagging data

 2. They are also used to accurately translate or transcribe audio clips and pictures, since these are activities that are better suited to humans than computers. These are used both for practical data conversion purposes, but also to improve upon and test the fidelity of machine learning algorithms.

3. They are also used to accurately translate or transcribe audio clips and pictures, since these are activities that are better suited to humans than computers. These are used both for practical data conversion purposes, but also to improve upon and test the fidelity of machine learning algorithms.

4. Identification of pictures by humans has been used to help in missing persons searches, though to little effect

5. It is a good platform for reaching a large population for social studies and surveys since they make it easy to offer monetary incentives.

There is a sort of a revolution when it comes to crowdsourcing and data collection. Advancements in technology have made it easier to get detailed insights quickly and reward those who provide them. 

What is data crowdsourcing?

Crowdsourced data collection is when researchers enlist the services or use the wisdom of a diverse group of people to research, survey or provide feedback, paid or unpaid usually via the internet. Crowdsourced data collection is gaining popularity because it is convenient, cheap and relatively fast. 

Considerations for crowdsourcing data

1. Ensure a large network of contributors

2. Follow network growth carefully. Crowdsourcing requires a crowd, not just a few people.

3. Consider the trade-offs between sample size and sampling issues. Crowdsourcing may not be the right option if you require strict sampling and data structure.

4. Request simple tasks from contributors. 

5. Ensure that the platform on which you are collecting crowdsourced data is well-tested.

6. Quantify trade-offs carefully. Will cost savings offset losses in precision or quality

In-house VS Crowdsourced data labeling 

In-house data labeling


1. Homegrown, consistent annotation processes can yield long-term reliability and success.

2. Annotation feedback loop allows you to constantly improve.

3. Strong quality control.


1. It is not always practical, depending on your data and company size.

2. It is expensive and time-consuming to build a good annotation process from scratch.

3. Tool sourcing is time consuming and expensive. Depending on data type and size, data may require high-level manpower to annotate.

4. Data labelled by in-house teams may be biased 

Crowdsourced data labeling


1. Worldwide annotation workforce available 24/7.

2. Highly affordable and quickly deployable

3. Can be utilized alongside in-house labeling or with a provider.

4. Using a diverse workforce reduces the risk of bias 

5. Allows you to handle culture- and language-specific annotation projects anywhere in the world


1. Quality control isn’t guaranteed.

2. Hard to achieve repeatable and consistent results over time.

3. Using an external workforce limits your team’s ability to learn and develop their own processes.

4. Can be high-maintenance and time-demanding to manage.

5. Many companies do not treat their workforce ethically and that same workforce has no job security

What are use cases of data crowdsourcing in AI

AI systems require these components in order to function effectively:

1. Clean, labeled data to help the system work accurately

2. Data science eto build effective models

3. Testing to make sure the system works as planned

Data is the life of AI systems. As ML algorithms are given more data, their accuracy improve. However, collecting a sufficient amount of real-world data to use in algorithm training is not that easy and it requires a high volume of data. Processing such a high volume of data with in-house resources is going to be expensive and time-consuming. So it may be best to crowdsource options when labeling data to train machine learning models.

Best data crowdsourcing platforms

1. Scale:

Scale focuses on computer vision applications, Scale offers data centric end-to-end solution and a suite of managed labeling services through its annotation API. They have systems to retrieve human insights and the ability to annotate large volumes of data at a high throughput.

2. Clickworker:

Clickworker provides support for specialized tasks. It also provides tools for mobile crowdsourcing. They use their global reach of crowdworkers to create, validate and label data reliably to give AI training data that shows the kind of diversity that makes your AI model versatile and trains your AI system to excellence.

3. Amazon Mechanical Turk:

Also known as MTurk, it is a popular crowdsourcing platform commonly used for data collection. On Amazon's MTurk, you can create and coordinate a wide range of human intelligence tasks (known as HITs). They have a diverse workforce with strict screening processes and training.

4. Lionbridge AI:

Lionbridge’s data labeling platform makes it easy to collect data samples from a large number of expert labelers in over 300 languages. With over 20 years of experience, they have optimized the process and built a data labeling platform to maximize efficiency and quality of data. 

5. Isahit: 

Isahit is the only ethical datalabeling company with a positive social impact on its workforce. isahit offers free training to its contributors, higher remuneration than its competitors and above all a caring community on which each contributor can rely. 

Isahit's data labeling platform uses a human in the loop approach with expert workers (HITers) who perform human intelligence tasks. It is a source of quick, high quality and cost effective data gathering and annotation services.


Data labeling is a very important stage of data pre-processing. Fortunately for data scientists, crowdsourcing is an efficient option for outsourcing high quantity data labeling tasks to an ever ready and skilled workforce. To unlock the greatest potential of crowdsourcing, create engaging tasks with specific goals in mind and reward contributors appropriately. Data crowdsourcing  can help you to solve your most complex business challenges quickly and more efficiently. 

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