Three ways to use AI in the field of development

in Welcome to the Tech Forum

7 Nov 2022

Using machine learning, influencing artificial intelligence

• Public leaders require assistance making decisions about how to spend resources efficiently to achieve the UN Sustainable Development Goals.

• Although there are few real deployments of artificial intelligence (AI) in the development industry, it presents untapped chances to produce fresh evidence.

• To take advantage of AI, public leaders must foster external development, acquire AI competencies, and foster an entrepreneurial culture.

Currently, the development sector spends more than $300 billion annually to fund almost 400,000 development projects across 167 nations. Development initiatives serve several objectives. Examples include constructing schools so that kids may receive an education, supplying medicines so that people can get better healthcare, or setting up solar panels so that more people can access power.

All development initiatives share the goal of advancing the Sustainable Development Goals (SDGs) of the UN and fostering a brighter future for people and the environment.

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AI improves decision-making in the field of development.

To properly match the plethora of international development initiatives to the requirements of nations and regions, however, is a significant problem for decision-makers in the development sector. Decision-makers in the development sector need evidence-based decision assistance to accomplish this. Evidence is currently primarily produced manually without the use of tools, such as surveys, which are costly and have a restricted scope.

Artificial intelligence (AI) has only lately gained traction in the development sector as a tool for guiding policy choices. This creates a world of new opportunities: With a startlingly high degree of accuracy and at a low cost, AI solutions can identify households with poor income levels, no access to energy, or at risk of food shortages. Thus, enabling better decision making

Several proof-of-concept initiatives have used AI to assist decision-makers in the development sector over the past few years. For instance, using the metadata from mobile phones, AI has been used to map poverty. Later, during the COVID-19 outbreak, AI was used to locate those in need and provide money directly to their phones, bypassing the customary administrative bottlenecks. AI has also demonstrated significant promise for tracking SDG development through the analysis of extensive satellite imagery. By identifying underdeveloped sectors and areas, one spin-off, Atlas AI, leverages this technology to encourage capital investment in emerging markets.

Other AI-based efforts concentrate on the supply side of development financing. To better coordinate development projects across countries and SDGs, for instance, and to enable independent verification of global climate finance flows, artificial intelligence (AI) technologies using natural language processing can analyze millions of written reports from development projects.

A few proof-of-concept initiatives aside, there are still few instances of true AI implementations in the development industry. We identify three main causes based on our personal experience working with development organizations: First, internal resources and AI expertise still need to be developed, which slows down the innovation process. Second, for AI professionals looking to have a significant influence, the development sector is, in theory, quite appealing. However, not many AI skills have far found their way into the field of development and frequently move on to positions in the business. Third, when AI alters decision-making processes, new organizational frameworks and procedures are needed.

Redesigning current decision-making processes in businesses is difficult, especially when using cutting-edge technologies like AI.

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1. Developing AI capabilities: Development organizations must establish internal AI competences if they want to implement and maintain AI solutions over the long term. To achieve this, firms must strike a balance between retraining current personnel and expanding recruitment to draw in the best AI talent. Determining new organizational roles (like AI engineers) with long-term, expert-focused career paths will be necessary in order to accomplish this. Despite this, the battle for top AI talent is fierce. However, development organizations are in a special position to provide worthwhile work tasks that can have a significant global influence.

2. Hackathons, where interdisciplinary teams tackle real-world AI challenges from the development sector, are one effective way to promote this process. This puts current employees at risk of AI-centered problem-solving, where AI experts learn about the particular difficulties faced by the development industry and how they may use their expertise to help to the creation of effective solutions. For instance, interdisciplinary teams work for two months on data science tasks presented by organizations like the World Wildlife Fund (WWF) and the German Agency for International Cooperation at "Hack4Good" hackathons (GIZ). On this basis, both internal AI competences and fresh AI expertise from the outside can be cultivated.

3. Fostering an entrepreneurial culture: Organizational resistance to change is one of the main reasons why businesses frequently struggle to adopt cutting-edge technologies like AI. This is especially true for "bureaucratic" development sector organizations with significant interdependencies and protracted decision-making processes that could "kill" creative ideas at an early stage. Similar issues have caused many businesses to develop organizational sub-structures that enable more dynamic procedures as well as quick idea testing and scalability.

Decision-makers ought to follow comparable guidelines. Different approaches may be useful depending on the organization. Making distinct, startup-like entities within an enterprise is one approach. For instance, the SDG Financing Lab was established by the Organization for Economic Cooperation and Development (OECD) as a new vehicle to group first-mover AI activities. This ultimately led to the creation of the OECD's first-ever AI solution: a dashboard for tracking contributions to the SDGs made by international development aid.

Another means of communication between entrepreneurs and development organizations is through accelerator programs. Such accelerator programs enable startups to maintain the advantages of their small size and organizational independence while giving them access to the resources and mentoring of a large enterprise (e.g., data, personnel, finances, access to operations). In order to help new entrepreneurs, fight world hunger, the World Food Programmer has established the WFP Innovation Accelerator. This program grants these startups access to the organization's worldwide operations. A number of novel AI-based solutions have been tested as a result, including the Hunger Map Live, which tracks the global food security in real-time, and The World Food Programma is guided by Optimus when creating and delivering food baskets in a cost-effective manner.

Developing a long-term outlook

To fully utilize the potential of AI, decision-makers should "act now" on the aforementioned tactics. In addition to this, it's critical that decision-makers in the development sector establish a long-term vision and a consistent set of tactics for incorporating AI into their companies. These tactics should be continuously improved upon as time goes on. Early success stories can aid in overcoming organizational resistance in this situation by persuading stakeholders and employees of the benefits of AI. Eventually, this will enable the scalability of AI technology solutions across entire businesses.

In recent years, AI has sparked a fundamental shift in how businesses make decisions. Now, AI is poised to have a similar influence on the development sector and support public officials through evidence-based decision making. We will soon observe that AI will play a crucial role in development organizations' decision-making. Therefore, emerging AI technologies will significantly aid in achieving the SDGs by 2030.