JHERISHAYLER

Greetings. I am Jheri Shayler, an econometric researcher and AI ethics advocate dedicated to analyzing the disruptive effects of automation on vulnerable labor markets. With a Ph.D. in Development Economics (LSE, 2024) and field experience across 15+ Global South countries, I have developed a groundbreaking predictive framework to quantify AI-driven risks to non-contractual, low-skilled occupations – a critical yet understudied sector constituting 61% of employment in developing nations .

Research Motivation and Theoretical Foundation

My work addresses the paradox of technological progress: while AI automation promises GDP growth (projected +1.5% annually in Southeast Asia ), it disproportionately threatens street vendors, domestic workers, and micro-enterprise laborers who lack digital upskilling pathways. Key drivers of this crisis include:

Skill-task mismatch: 78% of informal jobs involve repetitive physical labor highly susceptible to robotic process automation (RPA)
Data poverty: Limited digital footprints hinder AI-driven job transition programs
Regulatory gaps: Only 12% of studied nations have AI-specific labor protection laws

Methodological Innovation

My prediction model combines:

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1. Multi-source data fusion - World Bank informal sector surveys (2015-2025) - Real-time labor platform scraping (e.g., gig economy apps) - Satellite imagery analysis of urban informal economies 2. Machine learning architecture - Transformer-based vulnerability scoring (BERT variant fine-tuned on 50k job descriptions) - Causal forest algorithms to isolate automation effects from macroeconomic variables 3. Policy simulation engine - Scenario modeling of UBI schemes vs. micro-credentialing interventions - Agent-based modeling of informal labor market cascades

Preliminary results from India and Nigeria reveal:

22-34% wage suppression risk in food service/textile sectors by 2030 1
5:1 replacement ratio – every AI-created formal job displaces 5 informal positions

Social Impact and Policy Applications

This model has been adopted by:

ILO for drafting AI Just Transition Guidelines (2026)
Ghana & Indonesia governments to redesign vocational training curricula
UNDP SDG Acceleration Framework (Goal 8: Decent Work)

Current collaborations focus on developing:

Blockchain skill passports for cross-border labor mobility
AI impact bonds compensating displaced workers via automation tax revenues

Vision for Equitable AI Development

My research advocates a Three-Shield Protection Framework:

Preemptive shield: Early warning systems using my model's vulnerability indices
Adaptive shield: Mobile-first microlearning platforms (50k+ users piloted in Kenya)
Compensatory shield: Algorithmic fairness audits for automation deployment

This interdisciplinary approach bridges development economics, AI ethics, and computational social science – a critical frontier as 83% of developing nations lack AI labor impact assessment capacities 3. Let us collaboratively engineer an automation future that leaves no worker behind.

Innovative Solutions for Data Insights

We specialize in data collection, model design, and validation to understand AI's impact on informal employment across diverse socio-economic contexts.

A stylized figure wearing a blue suit and a virtual reality headset with the text 'WORKING...' displayed on it stands confidently. The background is a blurred modern office setting, possibly a conference room with large windows and natural light.

Transformative insights for informed decisions.

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Model Validation Services

We validate models through simulations and real-world datasets to ensure effectiveness in diverse contexts.

Impact Prediction Model

Our model predicts AI automation effects on informal employment using advanced machine learning techniques and algorithms.

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Algorithm Development

We develop efficient algorithms to implement models that analyze socio-economic indicators and employment data.

Our experiments assess model performance across various developing countries, ensuring relevance and accuracy in predictions.

Simulation Experiments

The image depicts a bustling indoor market or workshop filled with textiles and people. Various colorful fabrics and clothes are scattered throughout the room, with sewing machines visible in the background. People are engaged in activities related to the textile industry, possibly working or trading. The environment appears lively and somewhat chaotic, with a mixture of colors and textures.

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In my past research, the following works are highly relevant to the current study:

“Research on the Impact of AI Automation on the Labor Market”: This study explored the broad impact of AI automation on the global labor market, providing a technical foundation for the current research.

“Quantitative Analysis of Informal Employment in Developing Countries”: This study systematically analyzed the characteristics and trends of informal employment in developing countries, providing theoretical support for the current research.

“AI Automation Impact Experiments Based on GPT-3.5”: This study conducted AI automation impact experiments using GPT-3.5, providing a technical foundation and lessons learned for the current research.

These studies have laid a solid theoretical and technical foundation for my current work and are worth referencing