AI Data Bias and Economic Asymmetry: Challenges and Solutions for the Developing World


In the pulsating rhythm of the digital revolution, Artificial Intelligence (AI) has rooted itself deep within various sectors of the economy. Yet beneath this veneer of progress, a latent economic imbalance, potentially harmful to developing nations, lurks. This is primarily embodied in the form of AI data bias, an inequality in data used for training AI, which could precipitate significant economic disparities between advanced and developing countries.

The adverse implications of AI data bias might render AI technology less effective, and even inaccurate, in markets of developing nations. Specifically, AI algorithms trained predominantly on data from developed economies could fall short in comprehending, let alone addressing, unique challenges endemic to developing nations. This inadequacy may curb economic growth and innovation in these regions, subsequently amplifying the digital and economic chasm between them and their developed counterparts.

Such bias in AI emerges when the data employed to instruct AI models fails to represent the realities or conditions of developing nations. To illustrate, facial recognition technology in AI, often educated with data from developed nations where white individuals form the majority, may lack efficacy in identifying individuals with darker skin tones or unique facial characteristics from developing nations. The potential fallout from this can span various sectors, from security to consumer services, potentially leading to discrimination or unjust treatment.

Similarly, AI’s use in healthcare for diagnosing and predicting diseases may falter if the training is limited to data from developed nations. The resulting inefficiency in diagnosing or predicting diseases that are more prevalent in developing countries can compromise healthcare quality and outcomes therein.

AI’s ubiquitous presence extends to consumer services, where it generates product or service recommendations. Such recommendations, however, may harbor biases towards developed nations, subsequently impacting sales, consumer satisfaction, and economic growth in these regions. As AI-enhanced services and products, predominantly developed and regulated by firms in developed nations, become indispensable, developing nations may find themselves diverting substantial resources to acquire these services, thereby facilitating wealth transfer from developing to developed economies.

Data is the engine that powers AI, and large tech behemoths such as Google, Meta, Microsoft, Amazon, Alibaba, Baidu, and Tencent ByteDance, hailing primarily from developed nations, often amass colossal volumes of data from global users, including those in developing countries. With their unparalleled resources and expertise, these companies are capable of transforming this data into profitable AI services, which may lead to the predominant accrual of economic benefits to developed nations.

However, this is not a cause for despair. There are several solutions that can be implemented to address this economic asymmetry.

Technology and Expertise Exchange

The key to bridging the artificial intelligence (AI) gap lies in fostering active exchange of technology and expertise between developed and developing countries. By embracing and adapting cutting-edge AI innovations, developing countries can leapfrog their progress. Simultaneously, developed nations stand to gain deeper insights into the unique challenges and requirements specific to developing nations. The outcome is a more inclusive AI that caters to a diverse range of global needs.

A practical manifestation of this exchange could be realized through strategic placement of experts across borders. By deploying specialists from developed countries to work alongside local teams in developing nations, we foster an environment conducive to mutual learning. The knowledge and experience sharing from such endeavors is instrumental in capacity building and in devising AI applications that are relevant to the local context.

Further complementing this hands-on approach, we can also foster collaborative research programs between universities and research institutions from both sides. By facilitating the sharing of academic knowledge and practical expertise, we stimulate the co-creation of AI solutions. Ranging from fundamental AI research to applied AI, such collaborative projects not only advance scientific knowledge, but also ensure the development of solutions that address real-world problems in developing countries.

The combined effort of expert exchange and collaborative research forms a robust strategy for technology and expertise exchange, creating an ecosystem that advances inclusive and effective AI on a global scale.

Empowerment and Education

Education and empowerment in developing countries are critical for promoting participation in the digital economy. It’s crucial that communities are equipped with the knowledge and abilities necessary to comprehend, apply, and even innovate within the realm of AI technology. This approach will ensure they can maximize the benefits of AI, paving the way for broader economic growth and innovation.

One strategy to instill these skills is the cultivation of partnerships between technology firms and academic institutions. Through such collaborations, corporations could provide opportunities like internships, apprenticeships, or mentorship programs. These experiences supplement academic learning with practical industry insights, deepening students’ understanding of real-world AI applications.

In addition to fostering industry-academic partnerships, we should also advocate for more scholarships and grants in AI and related disciplines. These financial aids would allow talented students with limited resources to gain advanced knowledge and skills in AI. As a consequence, we could witness a surge in local AI innovations, further boosting the technology ecosystem in these regions.

Moreover, repeated practical exposure to AI through corporate engagement plays a pivotal role in nurturing future AI experts. The hands-on experiences gained from internships and apprenticeships allow students to put theoretical knowledge into practice, giving them a comprehensive understanding of the AI industry.

Inclusive AI Governance and Audit

Inclusive AI governance and audits are also vital. This involves creating a regulatory and ethical framework for AI that includes various disciplines and experts from developing countries. This will ensure that AI is developed and used in a fair, ethical, and responsible manner.

Steps to address this include forming an AI ethics committee consisting of various stakeholders, including technology experts, legal experts, ethics experts, and community representatives. This committee’s task is to review and evaluate the use of AI in the organization or country and provide recommendations on ethical and fair AI use.

The development of an AI ethics code must include basic principles such as transparency, fairness, privacy, and accountability. This code of ethics should guide everyone involved in the development and use of AI.

Training and education about AI ethics should be an integral part of the AI Governance program. This involves training for AI developers on how to ensure ethical and fair use of AI, as well as educating the general public on how AI works and how they can protect their rights and privacy.

AI audits involve regular review and evaluation of AI systems to ensure compliance with the established code of ethics and standards. These audits should be conducted by an independent third party to ensure objectivity.

Public participation is also crucial. Communities must be given the opportunity to participate in the decision-making process about the use of AI. This could involve public consultations on AI policy or the use of technologies such as blockchain to enable democratic voting or decision-making on AI issues.

AI Governance must be a global effort involving cooperation between various countries and international organizations such as UN organizations like UNESCO, or other independent and non-profit organizations like OpenAI, WEF, PAI, GPAI, etc. This can encourage the establishment of international standards for AI or cooperation in ethical and fair AI research and development.

Data Trading

My idea on the concept of data trading, similar to carbon trading, can be used to enrich AI datasets. Developing countries can “sell” their data to developed countries, which will enrich AI datasets and help create a more inclusive and effective AI. This will also provide developing countries with a new source of income and help narrow the economic gap.

This can be implemented in several ways, one of which is to create a data market where companies and individuals can buy and sell data. For example, a technology company in a developed country might buy data from a company in a developing country to train their AI models. This can help the technology company improve the accuracy and effectiveness of their AI, while the company in the developing country receives financial compensation for their data.

Another alternative is crowdsourcing, where companies or organizations collect data from a large number of individuals or groups. For example, a technology company might ask their users to share their data to train AI models. Participating users may receive compensation, such as access to premium services or other rewards.

This system is expected to provide an exchange and flow of resources from developed countries to developing countries that will reduce economic gaps. Conversely, companies in developed countries also get a richer data set to be analyzed by AI.

Building Digital Infrastructure

Developed countries need to help build strong digital infrastructure in developing countries with various cooperation schemes such as grants, soft loans, joint ventures, etc.

 This infrastructure will enable better access to AI technology and data and facilitate the exchange of technology and expertise. In addition, strong digital infrastructure will also enable developing countries to participate in data trading and obtain economic benefits from their data.

An example of implementation that can be done is building a data center. This is an important infrastructure that supports various digital services, from cloud computing to streaming services. Building local data centers can increase the speed and reliability of these services, as well as create jobs in the technology sector. Giant AI companies like Alibaba, Tencent, Microsoft, and Google seem to have started doing this in Indonesia, for example. This will be a win-win solution, as this cooperation will lead to data diversity.

Improving Digital Literacy

We need to improve digital literacy in developing countries. This involves education and training on AI technology and data, as well as the development of digital skills. With better digital literacy, people in developing countries will be able to use AI technology more effectively and participate in the digital economy.

The easiest thing to do as a start is a campaign to raise awareness about AI and data bias can help the community understand these issues and how they can impact their daily lives. This campaign could involve the use of social media, posters, or community events.

By improving digital literacy, we can help people in developing countries to take full advantage of AI and to participate in the digital economy.

Encouraging Innovation and Entrepreneurship

Finally, we need to encourage innovation and entrepreneurship in developing countries. This involves providing support and incentives for start-ups and technology companies in developing countries and collaborating with universities and research institutions to develop innovative and inclusive AI technology.

Collaboration with universities and research institutions from developed countries can help start-ups and technology companies in developing countries gain access to the latest knowledge and skills in AI. This could involve collaboration in research and development, or internship and job placement programs.

Mentoring the government of developing countries to create policies and regulations that support start-ups and technology innovation can create a conducive environment for entrepreneurship and innovation. For example, policies that facilitate the establishment of start-ups, or regulations that allow the use of data for AI development.

By implementing these solutions, we can turn the challenge of economic asymmetry into opportunities for growth and innovation. Let’s work together to realize this vision and create a more equitable digital future for all.

Tuhu Nugraha
Tuhu Nugraha
Digital Business & Metaverse Expert Principal of Indonesia Applied Economy & Regulatory Network (IADERN)


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