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Humanities (Economics, Geography, and History)
1. AI in Economic Forecasting: Can Machine Learning Predict Market Behavior Better Than Humans?
Field: Economics
Why it’s strong:
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Combines traditional economic modeling with data science.
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Lets you evaluate predictive accuracy of AI vs. human-based models (e.g., econometric models or expert forecasts).
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Connects to behavioral economics and information asymmetry.
Data ideas:
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Compare historical predictions from IMF, World Bank, or private analysts with ML model outputs trained on macroeconomic indicators.
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Evaluate accuracy using error metrics (RMSE, MAE).
Cautions:
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Be realistic — focus on interpretability and limitations, not just performance.
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EE-level analysis doesn’t require heavy coding; using published data or secondary sources is fine.
2. How AI Can Help Analyze Historical Data and Patterns
Field: History / Digital Humanities
Why it’s strong:
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Explores how historians use machine learning to detect patterns (e.g., migration, trade, political sentiment).
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Connects historical inquiry with digital methods.
Data ideas:
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Use digitized archives (newspapers, census, trade records).
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Analyze how AI uncovers trends human historians might overlook.
Cautions:
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Emphasize methodological change (how AI changes historical interpretation), not just the tech itself.
3. The Impact of AI Automation on Global Economic Inequality
Field: Economics / Geography
Why it’s strong:
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Deeply relevant — connects technological change to income distribution and development.
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Lets you explore geographical disparities (e.g., between countries or regions).
Data ideas:
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Use World Bank, IMF, or ILO data on employment, GDP per capita, and AI adoption rates.
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Compare across regions or income brackets.
Cautions:
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Make sure to isolate AI’s role from other automation or globalization effects.