Data Scientists and AI

Human-Machine Information Developers for Decision-Making
Erscheinungsjahr 2027
ISBN: 978-3-032-41182-2
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Paczkowski, Walter R.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Chapter 1. Background on Decisions: Their Complexities and Requirements.- Chapter 2. Technology and Its Advancements.- Chapter 3. Complex Systems: An Introduction.- Chapter 4. Moral and Ethical Issues of Decisions.- Chapter 5. Data Science.- Chapter 6. The Data Conundrum: Data vs. Information.- Chapter 7. AI : Some Background.- Chapter 8. Economics of Substitution and Complementarity.- Chapter 9. AI for Data Science.- Chapter 10. Enabling the Partnership.

Über Autor(innen)

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 (SAS Press, 2016), (Routledge Press, 2018), (Routledge Press, 2020), (Springer, 2021), (Springer, 2022), (Springer, 2023), and (O'Reilly Media, 2024).