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.
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