Crowdsourcing data annotation is the process of breaking down tasks like labelling, tagging, or categorizing data into smaller bits and distributing them to a large online workforce. These workforces typically work on the tasks through a crowdsourcing platform, which is like having an on-demand global team available to work on your data. The data sets can then be used for Machine Learning and to train AI models. AI is only ever as good as the breadth and accuracy of the data it is trained on, so accuracy and quality are vital. This Crowdsourcing Week blog looks at how crowdsourcing data labelling and data annotation works, the benefits and risks, some of the leading providers, and a set of first steps for newcomers.
How crowdsourcing data annotation works
You have data, which could be images, videos, text, or even audio recordings. You need it labelled for your AI to understand the data. For example, an image might need labels or annotations for the objects it contains.
The data annotation tasks are broken down into smaller pieces and distributed to workers on a crowdsourcing platform who complete these tasks for a fee.
Quality control measures are used to ensure the accuracy of crowdsourcing data labelling and annotations. This might involve having multiple workers complete the same task or using automated checks. However, the quality control measures can vary in their robustness.
Once the tasks are complete, you have a dataset with high-quality annotations ready to train your machine learning models.
Remarkable benefits of crowdsourcing data annotation
- Speed and Scalability: A vast pool of workers means faster turnaround times for data labelling tasks. Need to label a million images? Crowdsourcing can handle it, and you can easily scale up or down as needed through on-demand access to potentially global teams of workers.
- Cost-Effective: Compared to hiring an in-house team, crowdsourcing can be significantly cheaper. You pay per task completed, reducing overhead costs.
- Diverse Perspectives: With a global workforce, you get a wider range of viewpoints which can lead to more comprehensive and accurate annotations. Imagine getting cultural nuances for your AI from real people around the world.
Simplistic risks
- Quality Control: Ensuring consistent and accurate annotations when crowdsourcing data labelling from a potentially untrained workforce can be difficult. You’ll need to invest in quality control measures to avoid biasing your AI model with bad data.
- Data Security: If your data contains sensitive information, crowdsourcing platforms need robust security measures to prevent breaches. Make sure the platform you choose prioritizes data privacy.
- Bias: The crowd itself can be biased. If you are not careful, your data annotations might reflect those biases, leading to an unfair or inaccurate AI model. Careful selection of workers and task design will help mitigate this.
Top platforms for crowdsourcing data annotation
Here’s a look at some of the top platforms, mainly in North America and Europe, for crowdsourcing data annotation. Keep in mind it’s always wise to research available options for your own specific needs.
Amazon Mechanical Turk (MTurk) is a well-established platform with a massive workforce. It is good for simple tasks, though quality control can be an issue for more complex crowdsourced data labelling and data annotation tasks.
Formed in 2018, Labelbox offers a user-friendly interface with built-in quality control tools. Ideal for complex annotation tasks requiring higher accuracy. It can use external workers or internal employees for data labelling tasks. It claims to be particularly suited to teams looking for labelling solutions to build applications for e-commerce, healthcare, and financial services industries.
Headquartered in Canada, LXT offers AI-driven data services through its crowdsourcing platform. It claims to help companies enhance their AI and machine learning projects by providing labelled data. The list of data services offered by LXT include data collection, evaluation, annotation and transcription.
Scale AI is well known for its expertise in image and video annotation, with a focus on data security and regulatory compliance. Payment is on a ‘per label’ basis, not a rate for an amount of time. It provides clients with a diverse labelling workforce, ensuring accurate and efficient results. Clients include Toyota, General Motors, Lyft and Airbnb.
Online pharmacies and pharmacy data centres can automate their processes through data annotation. Image source: ScaleHub
ScaleHub safely taps into trusted networks of crowd contributors for the tedious and time-consuming task of image annotation. It is primarily focused on North America, although it began in Europe and has offices in Germany and Bulgaria as well as the USA and Australia. Particularly known for its expertise in image and video annotation tasks, smart algorithms and a proven quality control system within its crowd workforce ensure high quality image annotation and data labelling within guaranteed completion times. It has access to the collective intelligence of a global on-demand crowd of 2.3 million contributors.
Clickworker is a data annotation crowdsourcing platform that is based in the USA and Germany. It breaks down large projects into micro-tasks and distributes them to a global network of over 6 million workers in more than 130 countries to complete. It specializes in tasks such as AI data collection, data annotation, data categorization, and web research.
Hive is a UK-based platform with a focus on data privacy and GDPR compliance. Every task is sent to multiple contributors for independent corroboration of results, and every project is carefully Quality Assessed before delivery.
Neevo is a UK-based speech data capture company that specializes in collecting and annotating spoken word data. Its crowdsourcing model accesses a global pool of workers who transcribe and annotate speech data. Neevo’s data is used by businesses to train AI systems for a variety of applications, such as virtual assistants and chatbots.
Video annotation makes it easier for computers that utilize AI-powered algorithms to identify objects. Source: Kili
Kili was founded in 2018 and is based in Paris. It focuses on creating a data labelling platform for Machine Learning applications in computer vision and neuro-linguistic programming. With additional offices in New York and Singapore, the company caters to businesses aiming to develop reliable AI. Major clients include L’Oreal, Renault, and Airbus. Projects include enhancing technologies ranging from facial recognition to autonomous driving and predictive maintenance. Kili’s product suite includes tools for image, video, text, OCR, geospatial annotation, and data labelling.
DYNAMIX is a data annotation service provider based in Serbia that offers a wide range of services, including image annotation, text annotation, and video annotation. They use a crowdsourcing model to access a pool of qualified annotators from around the world. The process of labelling or tagging video content with relevant metadata has become increasingly important in various fields such as computer vision, machine learning, and robotics. In healthcare, video data annotation aids in medical imaging analysis, surgical training, and patient monitoring.
Taking your first steps
This is not exhaustive list of relevant platforms. Carry out research to compile your own shortlist and examine each platform’s features, pricing, and expertise to find the best fit for your project. Here are some tips for choosing a crowdsourcing platform to carry out data annotation.
- Read reviews and case studies to learn what other companies have experienced when working with different platforms.
- Compare pricing models, as some platforms charge per task, while others have monthly fees. Levels of quality control can vary. Choose the model that best fits your project budget. You could also look for free trials that many platforms offer so you can test out their interface and quality control features before committing.
- Consider the sensitivity of your data, and if it’s private, prioritize robust security on the platform.
- The complexity of the annotation task will influence platform selection. For simple tasks, crowdsourcing can easily work well. For complex tasks, consider platforms that use a stricter vetting process to recruit workers, and then also employ more robust quality control measures.
Keep in mind that whilst crowdsourcing data labelling and data annotation can be a powerful tool, it’s not a one-size-fits-all solution. By carefully weighing up the benefits and risks, considering your particular needs, and researching the options we have covered, crowdsourcing data annotation can be a valuable asset for your AI development.
0 Comments