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Keith Lee

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Keith Lee is Professor of AI and Finance at the Gordon School of Business, Swiss Institute of Artificial Intelligence (SIAI). His primary research lies in financial mathematics and AI-driven computational science, with a focus on quantitative modeling of complex economic and financial systems. His work integrates machine learning, stochastic modeling, and data-centric methods to study structural transformations in markets and institutions.

His recent work examines the broader socioeconomic consequences of artificial intelligence, including labor markets, public finance, demographic change, institutional adaptation, and the distributional effects of technological progress.

He holds a PhD in Mathematical Finance from Boston University, and previously earned an MSc in Finance and Economics from the London School of Economics. He completed his undergraduate studies in Economics at Seoul National University under the Korea Foundation for Advanced Studies scholarship program.

Keith Lee

Educational systems train habits of thought, not only knowledge Examination success does not necessarily produce analytical independence SIAI seeks to replace answer-oriented learning with causal inquiry A recent study published

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Keith Lee

Prediction markets as laboratories for uncertainty, sentiment, and regime change Student research converted into models, data systems, and institutional capability SIAI Labs as the bridge from academic work to a research-driven service

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Keith Lee

Human feedback does not eliminate endogeneity or causal confusion Lagged observations can transmit error as easily as information Econometric identification should become part of advanced AI training There is a phrase frequently used in economics: “econo

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Keith Lee

AI is creating a sharp labor divide between capital owners, stable workers, and those being pushed out Education policy must adapt to this new AI labor divide or risk permanent inequality Public finance and schooling must evolve together to prevent economic exclusion

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Keith Lee

Speculation cannot fix structural affordability Stablecoins may look stable but can shift systemic risk Real reform requires stronger incomes and safer credit systems In 2024, a study found that about 63% of

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Keith Lee

AI copyright disputes are shifting toward strict AI data governance and data provenance scrutiny Settlements and licensing deals now shape the legal landscape more than courtroom doctrine The future of AI regulation will depend on verifiable governance, not abstract fair-use theory

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Keith Lee

Inflation spreads faster because firms reprice in response to shocks, not calendars Energy and AI amplify this speed, but state-dependent pricing is the core driver Policy and education must adapt to inflation that moves in days, not months

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Keith Lee

The third AI stack is a political ambition, not an industrial reality China’s open-source push wins users, not hardware supremacy Europe and Korea must focus on interoperability and skills, not full-stack rivalry Between August 2

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Keith Lee

AI data centers are straining local power systems Donations cannot replace enforceable community agreements Real benefits require binding commitments to the grid In 2023, U.S.

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Keith Lee

Japan’s growth problem is not a lack of effort, but weak output per hour Extending work hours raises costs without fixing productivity or wages Policy should shift from time worked to skills, management, and productivity gains

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Keith Lee

Wearable AI is moving computing from screens to body-level devices Education policy must balance personalization with privacy and trust Early rules will decide whether wearable AI helps learning or harms it Initially, artificial intelligenc

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Keith Lee

AI transparency is a public good that cannot survive without explicit funding Unfunded openness will weaken Western firms against state-subsidized competitors Paying for transparency is the only way to keep AI markets both open and competitive

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Keith Lee

AI systems produce fluent language through probabilistic pattern learning, not through conscious awareness Equating parameter scale with human cognition confuses simulation with subjective experience Education and policy must treat AI as powerful tools, not emerging minds

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