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

Pros

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.

Cons

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

Pros

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

Cons

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.

Conclusion

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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