Research: Ethical AI-Driven Intelligence: Governing Data Collection, Risk, and Capital Allocation

This research project was developed as part of my application to a fellowship at a major university. It examines how AI-enhanced data collection and analysis are transforming open-source intelligence (OSINT), with particular focus on the risks introduced by speed, scale, and automation. As AI systems increasingly mediate how information is gathered, filtered, and interpreted, this work explores how human–AI teaming can amplify cognitive bias, distort signal detection, and accelerate decision-making beyond safe or accountable thresholds. Through analysis of emerging tools—including automated scraping systems, agentic models, and simulation environments—the project identifies where risks concentrate across the intelligence lifecycle, from data ingestion to operational use.

Beyond technical systems, this research investigates the upstream forces shaping these risks, particularly how capital allocation and product design incentives influence the development of intelligence technologies. It challenges the assumption that “human-in-the-loop” systems inherently provide oversight, instead emphasizing the need for meaningful human control in high-stakes environments. Drawing on experience across investigative analysis, global risk research, and venture-focused AI diligence, the project proposes a forward-looking framework for evaluating AI-driven intelligence systems, with implications for governance, investment strategy, and the ethical deployment of security technologies.