How Developing Countries Can Grow from Data Curation
The increasingly valuable Metaverse does not offer much of its wealth to the Developing world. The creation of Data Curation jobs in these countries can create even more wealth from scratch.
The Internet, Data and AI has long been amassing value, and as it stands, countries with limited access to technology, are not sharing in this. In recent years, jobs in Data & Analytics have grown in popularity and demand, as businesses and organizations around the world look to unlock the potential of their data. However, the vast majority of data curation and labeling jobs are currently located in developed countries, particularly in North America and Europe. This is a missed opportunity for many developing countries abundant with untapped talent. Some promising roles, that could generate income for these countries, include Data Labellers and Data Curators.
In short, these workers collect and sort data for companies and institutions. This is not a new role, but given the sheer number of ways to capture and use data in modern times, this role will becoming increasingly important. By building a strong workforce in these countries, organizations could benefit from such valuable skillsets. This could help to drive innovation and improve the quality of data-driven decision making, leading to better outcomes for everyone involved.
AI progress is limited by the availability of high-quality data. Collecting, cleaning, and labeling this data requires significant human effort and can be time-consuming and expensive. Only recently has there been focus on data quality over model optimization. Industry expertise is also necessary to ensure labeling consistency and evaluate label accuracy. Managing annotators for a project can also be challenging. These challenges can significantly slow down AI initiatives.
There is no shortage of ways Data Curators delvier value to companies and institutions. More importantly, the numerous benefits for Developing countries: a stronger economy, jobs for the people in these countries, and having an international presence, which gives them even wider opportunities for mobility. This helps the countries by creating jobs for the people within these countries and also brings in money for the economy. However, these programmes have often been met with criticisms of being unfair and unethical. Tech corporations have instances of underpaying workers and having intense workplace environments, due to the need for scale.
One salient problem is that Data Labelling can be tedious and repetitive. Data Labelers often spend hours doing the same task over and over again. This can be very boring and frustrating for them, which can lead to high turnover rates. Data Labelers also don't have much opportunity for growth or advancement within a company. So they often feel stagnant after a while, which can lead to burnout. All of these factors can make Data Labeling a tough job and lead to high turnover rates. So that's the biggest problem with the role that Data Labelers face. While is this true of current Data Labelling programms, it does not reflect all possible ones. There is a chance to offer specific lines of research and expertise to these workers.
Let’s take a random example: Dentistry. This would involve data labeler looking at datasets such as a person's oral health in order to find patterns that could lead to serious dental issues, such as images of cavities, broken teeth, or other problems related to oral health. This would be an interesting task because it would be challenging and fun to analyze someone's dental history and look for patterns that could lead to serious dental issues. So it would be great for Data Labelers who are interested in researching more serious dental issues or are really into dental health because they would enjoy this kind of research. It also enables communities facing particular plights to invest in jobs/tech that may help them overcome this. For example, a country with Dental problems.
But what happens when enough labeled data has been provided to the algorithms? Does that elimate all of the jobs created in these developing nations? No. Among other reasons, things like data drift and concept drift ensure there is always a need for new labelled data. They will never be replaced entirely by algorithms because of the fact that the data might change over time, which would make it difficult for the algorithms to learn new patterns. On the flip side, Data Labelers can help to train algorithms to help them recognize new patterns in the data. So Data Labelers will always be needed to keep the labels up to date and in line with the intentions of the people who constructed/sampled the data.
Of course, there are physical challenges in bringing these jobs to Developing countries. One of the biggest challenges is the lack of infrastructure and connectivity in many of these regions. Many people in developing countries lack access to reliable internet, which is essential for these roles. To overcome this challenge, organizations could invest in infrastructure development, such as building internet connectivity in remote areas. This could help to provide people with the resources they need to access these jobs, while also driving broader economic development. Organizations could invest in training programs to help people develop the skills they need to succeed in these roles. There could be sponsorships or partnership companies that help people in these countries and give them the funds they need to get started. This would be a great step forward, while giving the company some sense that they are investing in a data curation pipeline that may generate revenue for them one day. At least this would give opportunity for them to have a bigger stake on the world digital economy.
Bringing data curation and labeling jobs to developing countries could provide numerous benefits, both for the individuals living in these regions and for businesses and organizations looking to access a talented workforce. By investing in infrastructure development and training programs, organizations can help to overcome the challenges and unlock the potential of these regions. However, it is crucial that the Data and Analytics industry continues to work to serve these countries and populations, altruistically, regardless of the actual economic value or potential these nations appear to have.
Learn more about the Digital Landscape in Part 2: How Data Markets Democratize Ideas.