This book examines the nature and causes of technological change and complex systems; provides a definition of information, primarily Rich Information; introduces AI for Rich Information provisioning; discusses AI limitations such moral/ethical dilemma resolutions and hallucinations; and argue for the collaboration of human and AI Data Scientists. The technological change, while improving efficiencies and increasing economic growth and welfare, has unintended negative consequences. The primary, and unnoticed, consequence is the exponential increase in the size and scope of the complex systems at the base of our economy, which is itself a complex system. These systems are difficult (almost impossible) to understand, let alone manage.

The book uses a business exnterprise, as an example. An increased business complex system complicates business decision-making at all enterprise levels, thus pressuring Data Scientists for Rich Information crucial for those decisions. These are two unintended consequences of technological change. Concurrently, technologically-driven data collection methods (e.g., Wireless Sensor Networks) increase the size, scope, and structure of databases as troves of Rich Information. This information must be extracted, but this is not a trivial task because information extraction tools (i.e., software) and methods (e.g., machine learning), themselves forms of technological change, are challenging to use and require advanced training. These are other unintended consequences of technological change. While technological change seems to have only negative consequences for complex systems, it provides a solution: AI.

AI Data Scientist replaces a human Data Scientist in Rich Information provisioning. But, AI is not without issues: its inability to resolve moral/ethical dilemmas and a tendency to hallucinate information. The author argues for the collaboration, rather than substitution, of human and AI Data Scientists for supplying Rich Information for decision-making in increasingly complex business systems.

Walter R. Paczkowski is the founder of Data Analytics Corp., a statistical and data modeling consultancy. Dr. Paczkowski was a lecturer in the Department of Economics at Rutgers University for 38 years (now retired to focus on his writing). He published seven books, all focused on different aspects of Data Science: Market Data Analysis Using JMP (SAS Press, 2016), Pricing Analytics: Models and Advanced Quantitative Techniques for Product Pricing (Routledge Press, 2018), Deep Analytics for New Product Development (Routledge Press, 2020), Business Analytics: Data Science for Business Problems (Springer, 2021), Modern Survey Analysis: Using Python for Deeper Insights (Springer, 2022), Predictive and Simulation Analytics: Deeper Insights for Better Business Decisions (Springer, 2023), and Hands-On Prescriptive Analytics: Optimizing Your Decision Making with Python (O'Reilly Media, 2024).