Data Annotation Partner vs. Crowdsourcing: What is the best choice for your AI project?

Crowdsourcing has become an increasingly popular way to obtain data annotations for applications such as natural language processing (NLP) or computer vision. While it can be profitable and effective for accumulating large amounts of tagged data, it also presents risks that potentially increase the total cost of your AI projects.

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How is crowdsourcing used for data annotation?

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Crowdsourced data annotation is the process of obtaining tagged data by outsourcing the annotation (or labeling) task to a large group of contributors, usually via an online platform. Contributors are generally anonymous and may come from a variety of backgrounds and levels of expertise. The platforms that contributors use generally offer a user-friendly interface that allows them to access data and annotate it based on predefined criteria, such as tagging objects in images or transcribing speech into audio recordings. Annotations generated by contributors are then aggregated and used to train machine learning models for various applications, such as natural language processing and computer vision.

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Annotating data with crowdsourcing: what are the benefits?

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Crowdsourcing offers several advantages, including the ability to quickly obtain large amounts of tagged data at a relatively low cost. Crowdsourcing platforms can take advantage of a large number of contributors to annotate data, allowing for fast turnaround times and scalability. Crowdsourcing can provide a diverse range of perspectives and expertise, leading to more comprehensive and accurate annotations, and allowing for 24/7 annotations, increasing efficiency and reducing turnaround times. It can also promote data transparency and the democratization of access to digital work, allowing anyone with an Internet connection to contribute to the labeling process, regardless of location or socio-economic situation. In any case, this is what is proposed and put forward by these platforms, even if studies have since shown that the jobs created by temporary work platforms contribute more to the precariousness of the populations who use them.

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Why choose a dedicated partner for data annotation?

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Annotating data is a critical step in machine learning. A partner specialized in data annotation (like Innovatiana) is a company offering services dedicated to AI and data processing. For the most part, these partners use internal annotators trained with domain-specific expertise. Because of their industry expertise, education, and experience, they generally provide data annotations that are better, more accurate, and more consistent than crowdsourced annotations.

While crowdsourced data annotation is a popular option among Data Scientists, there are several reasons why you should consider using a data annotation partner with an in-house workforce:

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1. Extensive experience and expertise

Data annotation providers who employ trained annotators have extensive knowledge and experience in the tasks specific to the domain they are annotating. This expertise ensures that annotations are consistent, accurate, and of high quality, resulting in better performing machine learning models. In addition, the teams dedicated to your Use Cases monitor the services and can intervene regularly, as for any service provision activity, guaranteeing you continuity.

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2. Quality control process and SLA

Processes are in place to ensure that annotations are accurate and consistent. For the largest orders (several hundreds of thousands of data to annotate), most providers offer guaranteed SLAs for the accuracy of the annotation.

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3. Continuing education

Data annotation companies generally provide ongoing training and support to their annotators (with internal training, daily monitoring, an internal journey for Data Labelers to progress). Over the long term, these trainings and team monitoring contribute to improving the quality and consistency of annotation work, which results in more accurate machine learning models.

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4. More flexibility and collaboration

The specialists in image annotation, of video or text adapt their services to meet the specific needs of customers, providing data information via a “”Human-in-the-Loop“(HITL) and a proactive process to improve the performance of machine learning models.

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5. Data privacy and security

Data protection regulations require that personal data be protected, and data annotation partners should have strict policies and procedures in place to ensure that data is secure and confidential. Unlike crowdsourcing, the teams of these service providers are identified, trained, and made aware of information security issues.

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What are the 4 main risks of crowdsourced data annotation?

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While crowdsourced data annotation can be an effective way to obtain large amounts of labeled data, it has significant risks — such as inaccuracies, biases, privacy concerns, and security concerns — that need to be considered in the decision-making process. Here is a quick overview of these risks:

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1. Inaccuracies and Inconsistent annotations

Crowdsourcing platforms generally rely on a large number of anonymous contributors from a variety of backgrounds, who may not be familiar with the specific field or task. Since tasks are accessible to as many people as possible, the level of qualification is not always appropriate, which can lead to a multitude of errors corrected using a very large number of contributors... which increases costs, and can still lead to inconsistent or inaccurate annotations that can have a significant impact on the quality and reliability of the data used to train AI models.

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2. Biased annotations

This can happen when contributors have personal or cultural biases that affect their annotations. For example, someone from a particular cultural background may interpret an image or text differently than someone from another cultural background. This can have a significant impact on the performance of the resulting machine learning models, especially if these potential biases are not qualified before starting the annotation process. For some use cases, this has no impact (distinguishing a cat and a dog is universal!).

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3. Difficulties in evaluating the performance of annotators and in not reproducing errors

Iterating with crowdsourced annotators is often difficult because managing and coordinating a large number of anonymous contributors can be complicated. The turnover rate is also higher because contributors lose interest or move on to other projects, which can lead to delays. It can be difficult to ensure the quality of annotations by relying on a large, unverified group of contributors with minimal training and no identified functional expertise.

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4. Lower data security and confidentiality

When using anonymous contributors, there is always a risk that a contributor will accidentally or deliberately disclose personal or confidential information, which can have significant legal and ethical consequences. Additionally, crowdsourced annotators use their own hardware and infrastructure, which can lead to security breaches if they don't have appropriate antivirus software or if they don't regularly update or patch their machines and applications consistently.

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5. Crowdsourcing ethics

The use of crowdsourcing for data annotation raises significant ethical concerns. There is a risk of exploitation of contributors, who are often paid minimally for their work, which may not reflect the real value of their contributions to artificial intelligence projects. Additionally, the anonymity of contributors in crowdsourcing can lead to accountability and quality issues, as it is often difficult to ensure that annotations are done ethically and accurately. The ethics of crowdsourcing for data annotation depends on how it is managed and on protecting the rights and dignity of workers and data security, which requires appropriate oversight and regulation to ensure ethical practices in this area.

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In conclusion

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Using a data annotation partner offers several benefits, including higher quality annotations, more flexibility and collaboration, and a human-in-the-loop (HITL) approach at scale. When choosing a “Data annotation partner”, it is important to take into account its specific functional expertise, its quality control process, its privacy and security policy, as well as its ability to customize its services to meet your most specific needs.

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Why choose Innovatiana to annotate your data and accelerate the development of your AI products?

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Innovatiana offers leading data annotation solutions thanks to our ethical approach to AI, our experience and our functional expertise. We have developed a methodology to train annotators (or Data Labelers) and create the most advanced training data, highly focused on areas of functional application (medicine, architecture, legal, legal, real estate, etc.). We do this while maintaining a strong commitment to building an ethical AI Supply Chain! Learn more.