Are you still working on crowdsourcing?

The number of crowdsourcing participants called "data labels" who can work remotely has increased. DSLABGlobal developed an automated labeling solution to address the shortcomings of crowdsourcing, so let's take a look together.

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How crowdsourcing is coming to an end

​ ​ ● Intro
This blog post will examine the impact DS2.AI’s automated labeling solution can have on SaaS businesses and introduce the strengths of DS2.AI’s solutions with regards to crowdsourcing. ​ ​
What is crowdsourcing?
Crowdsourcing is the act of sourcing from the public(crowd) to carry out business-related activities. Through crowdsourcing, companies can use collective intelligence to find not only the variables that affect marketing, but also potential customers or human resources. From the public’s side, they can build career and gain financial benefits by selling their ideas to companies.

In the past, companies have adopted outsourcing methods commissioned by experts in the field, but today, since new communication methods let them approach an unspecified number of the public, they tend to adopt crowdsourcing, which is a lot cheaper and simpler. ​ ​ enter image description here

(Crowd sourcing; a way of creating and adopting ideas through collective intelligence)

​ ● Disadvantages of crowdsourcing and the strength of DS2.AI
Though the advantages of crowdsourcing are significant to consider, there are disadvantages as well.

Here 4 disadvantages of crowdsourcing that could arise in connection with data labeling, which was covered in the previous post,

  1. First of all, since most of the public are relatively non-experts, the reliability is low in areas where accuracy and expertise is the key.

  2. It is difficult to maintain relationships with unspecified public. In fact, it is often a one-time operation unless you have a continuous contract with the workers.

  3. There is a possibility of leakage of company’s critical data or information. Some industries don’t consider adopting crowdsourcing due to this leakage issue.

  4. From the company’s perspective, when commissioning simple and repetitive tasks to the public, the time required or the cost to be paid is not negligible.

Among these, the companies biggest concerns are 1, 3, and 4. It will be risky to proceed AI pre-work without stability or professional assistance.

Then how does DS2.AI's solution deal with that problem?

  1. First of all, we operate automated AI models with high accuracy in SaaS form. Therefore, it is possible to work immediately after purchasing online, which increases accessibility. Also, since we take the task's responsibility as a provider, the trust is guaranteed. Our AI models have more than 90% of test results.

  2. Users can purchase and use only the software they need, which can prevent burden for worker management and data leakage.
  3. There is no additional labor and cost since only our solution is needed, and the unit price of labeling itself is about 20% of that of third parties. This is significantly lower than competitors who automate part of the AI development process.

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(DS2.AI's solution approach to both automation and affordability)

The advantages of the solution are not only the benefits to companies, but also to individual crowdsourcing workers.
Currently, individual labeling workers face low-wage problems where competitors cannot earn big profits even if they work on detailed cursor tasks all day long on a small monitor except for some high-income earners.

If they link manual labeling with our auto labeling technology, they can do a lot of work with much less burden.​​

Check through the smart inspection system.

Some argue that it is difficult to trust the accuracy of the results in the practical stage when working with AI solutions, and has come up with a plan to review the results by introducing a smart inspection system.

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(Labeling AI - Smart insepction system algorithm)

As with the algorithm, once auto-labeling is completed, you can select Pass or Return by looking at the result.

All returned works are deleted and auto-labeled by creating a new artificial intelligence with passed data. If this process is repeated, high-accuracy labeling becomes possible, and inspectors can follow up with high-quality results without having to correct the wrong labeling data one by one.

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(Actual operation screen: checking labeling results, passing through and returning them.)


In this post, some of the possible side effects of crowdsourcing when labeling data and the our solutions to them were discussed.
DS2.AI’s use of SaaS form emphasizes the advantages of fast, accurate, and affordable labeling process. In the next post, we’ll be looking at how our AI models can be distributed other than SaaS forms. ​
Check Labeling AI that helps automatic data labeling !