Five Key Factors for Successful ML Integration in International Development
This article was written by Gratiana Fu and Greg Maly
Machine learning (ML) has been a widely discussed topic in the development sector due to its potential to transform how organizations approach complex problems. The integration of ML into international development work can help organizations to make more informed decisions, reduce costs, and optimize resources. However, integrating ML into an organization's work can be a complex and challenging process. In this article, we will discuss five considerations that international development organizations can take to prepare for ML integration.
1. Define the problem you want to solve
One of the most critical steps in preparing for ML integration is to clearly define the problem you want to solve. This involves understanding the scope of the problem, the available data, and the specific outcomes you hope to achieve. Without a clear understanding of the problem, it will be difficult to develop an ML solution that is effective and efficient.
A variety of complex problems can be solved with ML. One example is ML for informed decision support for farmers using climate and location-specific agriculture data. There are a number of factors that affect crops including air pollution, rainfall, and temperature, to name a few. ML can identify historical patterns in crop outcomes relative to these climate and social factors and provide farmers with tailored advice to mitigate potential risks.
2. Assess your data
It is important to assess the data that you have available. ML algorithms require large amounts of data to learn and make accurate predictions. It is important to ensure that your data is clean, consistent, and relevant to the problem you want to solve. Incomplete data and data used for purposes outside of the context in which it was collected can lead to biased or incorrect predictions.
Incorrect predictions can result in serious consequences. For example, ML is currently being used to provide on-demand diagnostics for healthcare workers in low-resource settings. A wrong diagnosis can be a fatal mistake. Knowing the ins and outs of your data is one key step you can take to prevent errors.
3. Build Internal Expertise
Building organizational knowledge about what you can do with machine learning can be a key step in helping your company leverage the power of this technology. This may involve hiring data scientists with expertise in machine learning, engaging with partners who can provide additional resources and support, or investing in professional development for existing employees. By building a strong understanding of the capabilities and limitations of machine learning, you can identify new opportunities to optimize business processes, improve project implementation, and drive innovation. Whether you're just starting out with machine learning or you're looking to expand your existing capabilities, investing in knowledge-building can help you stay ahead of the curve and unlock new possibilities for growth and success.
4. Develop a framework for responsible ML use
If an organization doesn't take ethical AI seriously, there can be serious consequences for both the organization and the people affected by its decisions. AI models that are trained on biased data or lack transparency can perpetuate existing inequalities and discrimination, leading to harm for marginalized communities. In addition, AI models that are poorly designed or implemented can result in significant errors or biases that can undermine trust in the technology and the organization. This can lead to legal and financial risks, reputational damage, and a loss of stakeholder confidence. By failing to prioritize ethical AI, organizations risk not only their own success, but also the well-being of the individuals and communities impacted by their technology. It's critical for organizations to prioritize ethical considerations in their AI development processes, from data collection and model training to implementation and ongoing monitoring, to ensure that their technology is trustworthy, equitable, and effective.
You can read more about ethical and responsible AI/ML principles here.
5. Pilot and scale your ML solution
Piloting a machine learning model or application for international development programs requires a comprehensive approach that considers both technical and non-technical factors. On the technical side, it's important to continuously review the problem(s) being addressed, the data available, and the specific machine learning algorithm being used. Additionally, it's crucial to test the model in a real-world setting to ensure that it's accurate and effective. This may involve partnering with local organizations to gather data and insights, as well as testing the model with target users to ensure that it meets their needs.
On the non-technical side, successful pilot programs also require a strong understanding of the cultural, social, and economic context in which they operate. This may involve engaging with local communities and stakeholders to build trust and understanding, as well as navigating complex regulatory and political environments. By taking a holistic approach that considers both technical and non-technical factors, organizations can increase the likelihood of successful machine learning pilot programs that have a meaningful impact on the lives of people in international development contexts.
In Practice
Integrating ML into international development work can have significant benefits for organizations, but it requires careful planning and preparation. At Exchange.Design, we support organizations who aspire to integrate AI and ML into their workflows through a combination of training and data reviews. We also collaborate with international development organizations to develop responsible ML frameworks that consider ethical and social implications. Finally, we provide technical support and expertise in piloting and scaling ML solutions for international development programs. By thinking through these five considerations, organizations can develop effective and responsible ML solutions that improve their ability to tackle complex problems.
