World Bank Group (WBG)
World Bank Group (WBG)
Description of Activities on AI
Project 1: Creating Global Public Goods – Famine Action Mechanism (FAM)
Continue leveraging the Famine Early Action Mechanism (FAM), a joint WBG-UN initiative, engaged in partnerships with global technology firms such as Amazon, Google, and Microsoft, as well as such data providers and technology experts to, support the development of AI/Machine Learning driven models predicting probabilities of food crisis. FAM illustrates the World Bank’s efforts to promote a preventative and preparedness approach to crises and is a concrete application of the Global Crisis Risk Platform (GCRP). The FAM also represents the deepening of partnerships across the humanitarian and development communities to address the most complex, multi-dimensional challenges of extreme poverty.
Work with the UN family to operationalize the Principles of Trustworthy AI by re-imagining the Health sector (ITU), focusing on AI Ethics and Humanity (UNESCO), and leveraging data and AI for insights on effective COVID-19 response (UN Global Pulse and WHO).
Participate actively in the Experts Group of the OECD AI Policy Observatory that aims to help countries enable, nurture and monitor the responsible development of trustworthy AI.
Project 2: Developing Knowledge and Policies
This year, the World Bank teams have actively looked at operationalizing the principles for trustworthy AI adoption and use by:
- Developing a policy-making guide for developing countries by examining new policy and regulatory pathways for harnessing AI to meet human and economic development objectives in developing countries and emerging economies by (1) analyzing emerging practices in the AI policymaking landscape globally, including developing countries, (2) identifying upsides and opportunities for development as well as downside risks such as inequality, threats to employment, privacy, security, and inclusion and human dignity), and (3) synthesizing these inputs into an actionable enabling policy making framework.
- Focusing on AI use in specific development sectors such as the preparation of a new GovTech report on ‘AI in Public Sector: Maximizing Opportunities, Minimizing Risks’. This report covers the ethical principles, opportunities and use cases of AI in the public sector. Also, there is an on-going work on Inclusive and Open AI for Human Capital that looks at AI use in education, health and social protection, to accelerate development and adoption and minimize risks.
- The International Finance Cooperation (IFC) has also prepared a series of analytical briefings to guide investments in emerging markets. The emphasis is on fostering innovations as well as eliminating obstacles to using and to the adoption of promising new technologies, i.e., AI. This series includes a focus on AI use in selected industries such as Agribusiness, Financial Services, Power sector, as well as safer, cleaner and more reliable Transport.
Project 3: Piloting AI in World Bank operations
The World Bank Group’s Technology & Innovation Lab serves as a knowledge and advisory hub around emerging technologies. The Lab explores and provides technology advice on emerging technologies’ potential for innovative problem-solving, and operationalization approaches in both WBG internal and external operations. The Lab has partnered with WBG teams across various sectors to solve development challenges by applying user-centric design and technology foresight, and through prototyping and exploration with AI capabilities [e.g., machine learning (ML), neural networks, natural language processing (NLP), assistive technologies (chatbots), etc.].
Challenges and Opportunities
- Data Privacy & Security: The reliance on data prompts the WBG to engage externally and obliges internally strict guidelines on data privacy, in addition to adhering to global standards of data privacy such as EU’s GDPR
- Data Scarcity: Lack of standardized datasets and thus volume requires data scientist to use new methodologies to attain enough data, these include: Supervised learning, Active Learning, and Transfer Learning methodologies
- Algorithm Bias: Biased datasets generate biased outputs. Human interaction to minimize outliers in datasets can minimize their influence, however, can be time consuming.
- Increased Demand: The COVID crisis has propelled digital transformation and create an increased demand from WBG client countries to gain a better understanding of key policy elements to enable AI adoption and expand AI-enhanced development opportunities.
- Internal Capabilities: WBG teams are steadfast to test theories, experiment usages and build evidence on harnessing AI for development.
- Trustworthy AI: The WBG teams are promoting the experimentation and implementation of responsible AI use. WBG will engage with policy makers around the world to build evidence towards a future with AI at the forefront of achieving SDGs.