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Predicting Performance of Black-Box Systems
Say, you are evaluating an AI-based platform that someone on your team highly recommends. How do you validate the system performance when you don't know what's inside the "box"? This talk will focus on an innovative methodology developed by Irene Aldridge to efficiently "unbox" an AI model. Gain confidence in dealing with AI models going forward. The talk will be based on a paper by Irene Aldridge available here.
Irene Aldridge is a recognized quantitative researcher, fund manager, and CEO of two startups, and an adjunct professor at the Cornell Financial Engineering Manhattan (CFEM) program and the Cambridge University Master's in Finance program (Judge Business School). She has previously successfully brought 2 platforms to market, Able Alpha and Able Markets, both in the Financial Services space. Over the years, Irene has also held various senior positions in most aspects of financial services, beginning with back office with large scale integration of financial systems and mission-critical security implementations, through system architecture for distributed web-secure applications, through risk management where she ran quantitative teams, through front office and trading, where she developed and built out cutting-edge quantitative financial tools for designing, trading and marketing of financial products.
Ms. Aldridge holds a BE in Electrical Engineering from Cooper Union (NYC), M.S. in Financial Engineering, MBA from INSEAD (France), and has studied in two PhD programs: Operations Research (Columbia University, IEOR, ABD) and Finance. To date, Irene has authored or co-authored six books (all published by Wiley), including Real-Time Risk (2017), High-Frequency Trading (2nd ed., 2013), and multiple research articles. Irene’s most recent publications include: “Synthetic KYC: Detecting Irregularities and Money Laundering on Blockchains” (patent pending, presented at Columbia Math Finance Seminar, INFORMS Security 2024, University of Florida Blockchain conference), “The AI Revolution: From Linear Regression to ChatGPT and Beyond and How It All Connects to Finance” (2023, Journal of Portfolio Management) and Big Data Science in Finance (2021).
