Data Labeling Industry: Is Crowdsourcing for AI the Only Model?
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Using data annotation services: a necessity for those who want to develop AI products?
Artificial intelligence (AI) has become an increasingly present topic of discussion in our society in recent years, stressing the importance of Sourcing ethical and responsible in the field of information technology. Recently, you've probably tested ChatGPT, from OpenAI, who blew your mind. However, according to the article ”AI Isn't Artificial or Intelligent“published by Vice, AI is not artificial or intelligent in the sense that we usually mean it.
It must be said that AI is actually a tool created by man to accomplish specific tasks, often through outsourcing and Crowdsourcing in areas such as Data Labeling. Its definition is that it has no conscience or will of its own, and cannot be considered an “intelligent entity” in its own right. AI is simply programmed to follow instructions given to it, and cannot think independently or make decisions independently. In short — it's a computer program like any other!
The impact of Crowdsourcing in the AI industry is undeniable. This concept, which consists of drawing on a large community to solve problems or complete tasks, is at the heart of many open innovation initiatives. The Crowdsourcing makes it possible to bring together ideas, knowledge and resources in an effective way, drawing on the contributions of many individuals around the world.
Social and ethical problems in outsourcing image annotation tasks?
It is important to note that AI can also cause social and ethical problems. For example, automating certain tasks may result in the elimination of some jobs, which may impact workers and their lifestyles. It is therefore important to think about how AI can be used in a responsible, equitable, and ethical manner, in order to minimize potential risks for individuals and society. However, we must minimize what we sometimes hear about AI (“artificial intelligence will eliminate our jobs, tomorrow I will be obsolete!”) : with AI, jobs that do not exist today will emerge and will create as many opportunities all over the world.
AI can also have significant positive externalities, creating new opportunities in various fields, including in developing countries. One of these positive externalities is the potential for job creation linked to AI (paradoxically). While some tasks can be automated, new jobs are emerging to design, develop, maintain, and supervise AI systems. Additionally, the massive data needed to power AI algorithms can be collected, annotated, and managed by human workers, creating jobs in data annotation and data quality management.
In developing countries, AI offers new economic opportunities. Businesses can outsource AI tasks, such as data or image annotation, to workers around the world, thus offering income opportunities for people with access to the Internet, even in remote areas. This work should not be considered thankless : it is a bias of privileged countries, which perceive annotation tasks for AI as “micro-tasks”, giving them little importance or credit in the AI development process. However, it is a necessary job for the AI revolution, which few individuals in the world are ready to do.
💡 It is essential to ensure that these opportunities are accessible in an equitable manner and that the benefits of AI are not only concentrated in certain regions or between certain populations.
What is the difference between Data Labeling Outsourcing and Crowdsourcing?
What is Data Labeling?
We repeat it often in this Blog, you get it, the Data Labeling is a critical process in the field of artificial intelligence (AI). It consists of label data for use in an AI model. The Crowdsourcing is increasingly used to produce such data labeling tasks in a short time frame. This is the dominant trend in the AI market, to produce data that can be used by models. If some people think that Data Labeling is dead with LLMs (Large Language Models), the reality is more complex: try asking GPT-4 to draw a Bounding Box on a very simple image, you may be surprised...
In short, what is the Crowdsourcing and how can it impact AI?
Why the Crowdsourcing for AI?
The Crowdsourcing is not a new concept: It is a strategy of data collection almost as old as the Internet, which involves relying on the contribution of numerous individuals to solve a problem or complete a task. This can be done online, via dedicated platforms, or using traditional methods such as surveys. The Crowdsourcing was widely popularized with platforms like Wikipedia, which allowed thousands of contributors to share their knowledge on a given topic.
Crowdsourcing is probably the best method to build an AI encyclopedia
The democratization of AI is comparable to the creation of a global encyclopedia through crowdsourcing. Just as Wikipedia revolutionized access to information, the Crowdsourcing in AI provides access to a diversity of data and perspectives that are essential for the development of inclusive and equitable technologies.
The Crowdsourcing, as a key open innovation strategy, is essential for the development of AI products and has proven to be particularly effective in the context of the continuous updating of algorithms and systems. The concept of Crowdsourcing, by its very definition, invites a collaborative and distributed approach, making it ideal for projects that require a wide range of data and perspectives.
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The Crowdsourcing can be an effective way to gather ideas, knowledge, and resources to complete tasks that would be difficult or expensive to do in a traditional way. Applied to Artificial Intelligence, it involves bringing together tens or hundreds of Data Labelers, generally untrained and from low-income countries, to invite them to work on a use case (for example: label 5000 vehicle images according to specific criteria). This approach has many negative aspects, with a social and ethical impact and a precarious working conditions for many people.
Here is an overview:
Exploitation of workers (Data Labelers or Data Labeling specialists)
One of the main problems of Crowdsourcing is that it can lead to the exploitation of workers, especially in low-income countries. Some platforms of crowdsourcing offer tasks to be carried out in exchange for remuneration, but this remuneration can be very low and does not reflect the real value of the work done. There can be a real gap between the work done by the Data Labelers teams and the low remuneration received. In addition, these platforms may not offer stability, social protections, or rights to workers, which may result in their situation becoming precarious. Although the Crowdsourcing To reduce costs and speed up production, it is essential to adopt an ethical and responsible approach, ensuring that workers are fairly paid and that their working conditions are dignified.
A negative impact on diversity and inclusion... and biased AI models
The Crowdsourcing may also have a negative impact on diversity and inclusion. Indeed, some platforms of Crowdsourcing may be dominated by certain populations, which can lead to a bias in the tasks proposed and in the way they are carried out. This can have negative consequences for marginalized or under-represented populations, who may be excluded from these collaborative processes.
The spread of fake news
Finally, the Crowdsourcing can be misused to Spreading false information or dangerous ideologies. Indeed, the participation of many people can give the impression of the existence of a consensus on a given subject, while it may be false information or manipulation. This problem is particularly worrying in the current context, where the rapid dissemination of fake news can have serious consequences on the lives of populations, in particular with regard to health or safety.
Should we do without data annotation services for AI?
The answer is “no”! Even in the face of ethical and social challenges, it is essential to recognize the existence (and importance) of Crowdsourcing in the process of developing AI products. Ethical and responsible solutions exist and must be explored to ensure a respectful production chain, Sourcing Data until the models are fed with annotated data.
Data Labeling, while time-consuming, is essential to ensure the effectiveness of AI. Mislabelled data can lead to erroneous results, stressing the importance of regular updating and careful verification of data. It is important that the Data Labeling process is carried out rigorously, by ethically involving all workers in the AI product construction chain.
“We need to think seriously about the human workforce that is in the AI supply chain. This workforce deserves to be trained, supported and paid to be ready to do important work that many may find tedious or too demanding“
Quote from Mary L. Gray and Siddharth Suri, authors of the book “Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass,” in an article published in 2017 in the Harvard Business Review.
What alternative (s) to Crowdsourcing for AI? Why choose specialized service providers?
In the rapidly evolving world of artificial intelligence (AI), the quality of training data plays a major role in the success or failure of an AI model. The Data Labeling process, which is essential for preparing this data, requires precision and expertise that only specialized providers can offer. This is where the importance of partners like CentaurLabs or Innovatiana, specialized in medical annotation, is becoming obvious.
Expertise at the heart of AI annotation
Data Labeling is much more than a simple administrative task; it is an operation that requires a thorough understanding of the field of application (medicine, finance, heavy industry, fashion, etc.). Specialized service providers not only provide technical expertise in data classification and labelling, but also in-depth knowledge of the sector concerned. In the case of medical annotation, for example, subtle nuances can make all the difference if the tool is used as a decision aid, for diagnosis.
CentaurLabs: a specialized model for medical annotation
CentaurLabs, a company that specializes in medical data annotation, perfectly illustrates the importance of expertise in the field of Data Labeling. By harnessing the skills of medical professionals, CentaurLabs ensures that annotated data is not only accurate, but also relevant and reliable for medical AI applications. This precision is critical, as errors in annotated medical data can have direct consequences on patients' lives and health.
Why choose specialized service providers?
Data Accuracy and Quality:
Specialized providers guarantee high precision in data annotation, which is crucial for the performance of AI models. This precision is especially important in sensitive areas like medicine, where mistakes can have serious implications.
Time saving:
By outsourcing Data Labeling to experts, businesses save valuable time and effort that can be better invested in other aspects of their AI projects.
Compliance and ethics:
Specialized providers are often better equipped to navigate complex regulations and ethical considerations, especially in regulated areas like healthcare.
Access to specific expertise :
Providers like CentaurLabs offer access to experts in specific fields, which improves the quality of annotations and, therefore, the performance of AI models.
Scalability and flexibility :
Specialized service providers can manage large volumes of data and adapt to the changing needs of projects, which offers businesses great flexibility.
💡 In conclusion, outsourcing Data Labeling work in a low-income country is a considerable responsibility: we at Innovatiana are well aware of this. We are implementing ways to put people and ethics at the heart of your AI efforts ! Indeed, it is essential to ensure that Data Labelers are fairly paid and that the processes are inclusive and do not spread false information or biased content.
