Global Economic Development
Many of the themes and challenges that we discussed above come together when analyzing the socioeconomic effects on developing countries. The workforce of developing countries may suffer from a retrenchment of outsourcing as many simple cognitive tasks that used to be performed in developing countries — for example, in call centers –— can be automated with LLMs. This may adversely affect the economies of the poor countries (Georgieva, 2024).
ENTITY
3 - Other
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit1500
Domain lineage
6. Socioeconomic and Environmental
6.2 > Increased inequality and decline in employment quality
Mitigation strategy
1. Strategic Workforce Transformation and Upskilling Prioritize national and corporate investment in training programs to transition the workforce from performing "simple cognitive tasks" to high-value roles focused on LLM management, exception handling, data quality assurance, and hybrid design. This strategy aims to leverage automation to remove routine tasks, augment human expertise, and elevate the required skill level for remaining employment, thereby supporting wage and employment stability in the affected economies. 2. Establish Contextual LLM Policy and Investment Mechanisms Implement targeted macroeconomic and social policies, potentially utilizing LLM-assisted analysis, to guide responsible AI deployment in developing nations. This includes creating new economic frameworks that actively support diversification away from traditional outsourcing, ensuring that the benefits of LLM applications reach large employment sectors, and enforcing responsible guardrails to promote equitable and sustainable growth. 3. Mandate Ethical and Secure AI Outsourcing Governance Develop and enforce industry-wide governance frameworks for Business Process AI Outsourcing (BPAO) that require comprehensive risk management, data privacy protection, and a systematic, evidence-based review of the socioeconomic impact of automation on local communities. This ensures legal compliance and prevents unchecked automation from undermining intellectual property and social equity by mandating transparency on how human expertise and AI are blended in the workflow.