How to outsource annotation work? 7 tips

Today, AI, which is rapidly expanding and being applied in various fields, requires data annotation for the development and machine learning of AI. In annotation, it is necessary to tag a large amount of data, and in some cases, it can take several weeks to several months. When performing such work in-house, it can result in huge costs, including resource allocation and management of quality and progress. In this article, we will discuss the benefits of outsourcing annotation work and seven points to consider when doing so.

Table of Contents

1. Pros and Cons of In-house Data Annotation
2. Benefits of Outsourcing Data Annotation
2-1. Cost Reduction and Time Shortening
2-2. Management Load Reduction
3-3. Improvement and Stabilization of Quality
3. 7 Tips for Choosing a Data Annotation Outsourcing Partner
3-1. Is it suitable for our company's requirements?
3-2. Expertise and Experience
3-3. Quality Management
3-4. Security
3-5. Scalability
3-6. Communication
3-7. Price and Price Conditions
4. Human Science's Achievements
4-1. Project Overview
4-2. Challenges and Suggestions
4-3. Effects after Introduction and Customer Feedback
5. Data Annotation Outsourcing Service by Human Science Co., Ltd.

1. Pros and Cons of In-house Data Annotation

Here, we will look at the advantages and disadvantages of internalizing data annotation.

Benefits
If internalized, there is less risk of data leakage to external sources, making it possible to operate relatively safely from a security and privacy standpoint. Additionally, the annotation process can be designed flexibly, allowing for quick response to changes in annotation requirements or specifications, as well as real-time monitoring of quality and productivity. By allocating resources to manage annotation and human resources, there are significant benefits to be gained from internalization.

Disadvantages
When AI engineers perform data annotation themselves, it not only causes delays in AI development, but also leads to an increase in development costs due to the high cost of labor. On the other hand, even if specialized resources for data annotation are secured, if the annotation tasks are not continuous, there will be idle time and waste, and managing and controlling these resources will also incur significant costs.
In addition, ensuring the quality and productivity of data annotation using a large number of resources requires experience and skills in annotation management, which are different from those needed for development. In the case of in-house data annotation, it is often difficult to ensure quality and productivity without these skills, and in fact, we often hear from our clients that "we tried to do data annotation in-house, but it didn't work out and we lacked the skills and knowledge, so we consulted with your company."

2. Benefits of Outsourcing Data Annotation

Although it overlaps a little with what was mentioned in the previous paragraph, there is not much expertise required for the actual data annotation work. However, in order to properly perform data annotation, expertise in managing personnel and tasks specific to data annotation is necessary. This is different from the expertise of a development engineer, so having engineers perform data annotation work in parallel with their main duties within the company would not efficiently utilize their expertise and would result in decreased productivity in their main engineering tasks.

Data annotation does not require much expertise in the work itself, but it does require expertise in managing personnel and tasks. Unlike the expertise of a development engineer, having engineers perform annotation work in parallel with their main duties means that their expertise cannot be efficiently utilized, resulting in decreased productivity in their main engineering tasks. Even if a company gathers personnel for annotation work, a large number of personnel is necessary to perform a large amount of annotation, and managing quality and progress incurs a lot of costs and labor. Therefore, outsourcing annotation work allows engineers to focus their expertise on their main tasks, resulting in potential cost reduction and increased productivity.
Related Blog
>>7 Tips for Successfully Conducting Data Annotation

2-1. Cost Reduction and Time Shortening

In addition to what has been mentioned so far, the unit cost of development engineers and annotators is significantly different. Therefore, even when including management fees and profits for outsourcing vendors, it is often possible to reduce costs more by performing annotation in-house, and the cost reduction effect becomes more significant as the amount of work requested increases. In addition, many annotation vendors have specialized experience in annotation work, so they have know-how to improve productivity per person without relying solely on increasing personnel. Therefore, by outsourcing, it is possible to not only secure time within the company, but also expect to shorten the overall delivery time of annotation work while keeping costs down.

2-2. Management Load Reduction

Data annotation, which deals with large amounts of data, is commonly carried out with a large number of personnel. In order to ensure quality and complete the work on schedule, various types of management are necessary, such as preparing work manuals and managing progress, regardless of the type of annotation. Especially in the early stages of the work, there are many questions and answers with the data annotators. This management not only takes more time than expected, but also requires experience and expertise to proceed efficiently. If you can entrust it to an experienced external vendor, these management burdens will be reduced.

2-3. Improvement and Stabilization of Quality

Despite the advancement of automation, data annotation is still mainly done manually, and the experience, skills, and variations among data annotators greatly affect the overall quality of the training data. By outsourcing to a vendor who can effectively manage experienced data annotators, high-quality annotation based on specifications and work instructions can be achieved. Outsourcing to such vendors with experienced data annotators can ensure stable and high-quality training data.

3. 7 Tips for Choosing a Data Annotation Outsourcing Partner

When outsourcing data annotation, choose a company that meets the requirements of your own AI development, such as quality and security. Here, we will explain the seven points to consider when choosing an outsourcing partner.

3-1. Is it suitable for our company's requirements?

There are various requirements such as types of annotations, data formats, tools to be used, and project scale that are necessary. It is important not to forget to check whether the outsourcing company is suitable for your requirements, as it is often overlooked due to being taken for granted.

3-2. Expertise and Experience

Data annotation for medical images, text, and language is a specialized task that often requires expertise. It is important to confirm the track record of annotation that fits your company's needs. A company with high expertise and experience in specialized annotation can provide appropriate annotation according to your requests, and even offer advice if your company lacks knowledge and experience in such annotation.

3-3. Quality Management

Quality management is important to ensure proper execution. It is also important to confirm the project management system, checking methods, and checking system, as well as thoroughly managing change information related to specification changes. It is also important to confirm the process and information management methods, such as how to communicate and reflect information to data annotators and how to confirm it, when providing feedback such as corrections and changes from our company.

3-4. Security

It is important to ensure that proper security measures are in place. Some companies may only support remote work by cloud workers. It is also possible that remote annotation may not provide sufficient security measures or meet the company's security requirements. In such cases, it is important to confirm if the company also supports on-site work such as security rooms or on-site work at the client's location. It is also important to choose a company that has implemented comprehensive security measures, including security education for workers and a well-established information security management system, not just on the hardware side.

3-5. Scalability

In the PoC stage, it may not be given much importance, but it is important to consider the possibility of expanding the scale of data annotation in the next phase. Confirm the number of personnel that can be secured by outsourcing and the delivery date for the expected scale of data annotation. It is also important to confirm if urgent situations can be handled.

3-6. Communication

In data annotation, even after requirements definition and specification creation, various exceptions and edge cases may arise as work progresses. There are often frequent exchanges and feedback with outsourcing partners, so it is important not only to have smooth communication, but also to efficiently manage and update communication and information. It is also important to confirm whether information can be shared appropriately and efficiently through communication methods tailored to the needs, such as using chat tools and centralizing information management.

3-7. Price and Price Conditions

The method of presenting the price unit varies depending on the data quantity, delivery date, check method, and number of objects per file when selecting a subcontractor. It is recommended to request estimates from multiple companies with the same conditions. In addition, it is important to compare prices considering the conditions of the work location, such as remote work and security rooms, as they often affect the price.

4. Human Science's Achievements

Here we will introduce some examples of our past achievements. We hope you will find them helpful as a reference when considering outsourcing.

4-1. Project Overview

Classification Annotation for Conversation Text

4-2. Challenges and Suggestions

Task

When consulting with us, our clients often face the following challenges:
・It is their first time outsourcing data annotation, and they are concerned about pricing, quality control, checking system, and whether our feedback and requests will be accurately reflected.
・Data annotation is often ambiguous and does not have a definitive correct answer. Therefore, there is a high possibility of variation among annotators, making it difficult to determine how to annotate accurately and how to check for accuracy in order to obtain precise training data.

Proposal Details

Based on these customer challenges, we made the following proposals:
・Proposal for quality management, reflecting feedback, and proposing a management system. Flexible changes to the estimate content based on clear conditions and unit prices.
・For annotations with high ambiguity and no absolute correct answer, we propose a triple pass + check where three annotators (experienced in the same type of work) perform the same annotation, instead of the usual check for correctness.
・Creation of annotation specifications by our company and alignment with the customer.
・Establishment of a quick feedback system through chat with the customer.

4-3. Effects after Introduction and Customer Feedback

"We are very satisfied with the quality of the annotations and your company's response during the project. Your prompt response to our requests and revisions during the process was greatly appreciated. From the initial conversation, we felt that Human Science was a experienced professional and we could trust them. That's why we decided to request their services, and we are glad we did. "

5. Data Annotation Outsourcing Service by Human Science Co., Ltd.

Rich track record of creating 48 million pieces of teacher data

At Human Science, we are involved in AI model development projects in various industries such as natural language processing, medical support, automotive, IT, manufacturing, and construction. Through direct transactions with numerous companies including GAFAM, we have provided over 48 million high-quality training data. We handle various annotation projects regardless of industry, from small-scale projects to large-scale projects with 150 annotators. If your company is interested in introducing AI models but unsure of where to start, please consult with us.

Resource Management without Using Crowdsourcing

At Human Science, we do not use crowdsourcing and instead directly contract with workers to manage projects. We carefully assess each member's practical experience and evaluations from previous projects to form a team that can perform to the best of their abilities.

Utilize the latest data annotation tools

One of the annotation tools introduced by Human Science, AnnoFab, allows customers to check progress and provide feedback on the cloud even during project execution. By not allowing work data to be saved on local machines, we also consider security.

Equipped with a security room within the company

At Human Science, we have a security room that meets the ISMS standards in our Shinjuku office. This allows us to provide on-site support for highly confidential projects and ensure security. We consider confidentiality to be extremely important for all projects at our company. We continuously provide security education to our staff and pay close attention to the handling of information and data, even for remote projects.