Rational Machines and Artificial Intelligence
Rational Machines and Artificial Intelligence by Tshilidzi Marwala, published by Elsevier Science on March 31, 2021, spans 270 pages and is presented in English. This book explores the integration of intelligent machines into various aspects of society, emphasizing the role of Artificial Intelligence technologies, such as deep learning, in decision-making processes. Marwala examines the necessity for these decisions to be explainable, particularly in light of regulations like the General Data Protection Regulation (GDPR), and argues that rational explanations are essential to uphold the integrity of machine-generated decisions.
Readers will find an in-depth analysis of machine rationality, comparing it to the rationality frameworks established by Nobel Laureates Herbert Simon, Richard Thaler, and Daniel Kahneman. The book addresses critical questions regarding the reliability and fairness of decisions made by intelligent machines, introducing concepts such as rational opportunity costs and flexibly bounded rationality. Through examples from biomedical engineering, social sciences, and finance, Marwala investigates whether collective decision-making among intelligent machines yields greater rationality than individual machines, while also discussing the implications of advancements in technology on the boundaries of machine rationality.
Official synopsis Publisher
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts.
- Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective?
- Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions
- Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets
- Discusses the application of Moore’s Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
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