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The twenty last years have been marked by an increase in available
data and computing power. In parallel to this trend, the focus of
neural network research and the practice of training neural
networks has undergone a number of important changes, for example,
use of deep learning machines. The second edition of the book
augments the first edition with more tricks, which have resulted
from 14 years of theory and experimentation by some of the world's
most prominent neural network researchers. These tricks can make a
substantial difference (in terms of speed, ease of implementation,
and accuracy) when it comes to putting algorithms to work on real
problems.
The development of "intelligent" systems that can take decisions
and perform autonomously might lead to faster and more consistent
decisions. A limiting factor for a broader adoption of AI
technology is the inherent risks that come with giving up human
control and oversight to "intelligent" machines. For sensitive
tasks involving critical infrastructures and affecting human
well-being or health, it is crucial to limit the possibility of
improper, non-robust and unsafe decisions and actions. Before
deploying an AI system, we see a strong need to validate its
behavior, and thus establish guarantees that it will continue to
perform as expected when deployed in a real-world environment. In
pursuit of that objective, ways for humans to verify the agreement
between the AI decision structure and their own ground-truth
knowledge have been explored. Explainable AI (XAI) has developed as
a subfield of AI, focused on exposing complex AI models to humans
in a systematic and interpretable manner. The 22 chapters included
in this book provide a timely snapshot of algorithms, theory, and
applications of interpretable and explainable AI and AI techniques
that have been proposed recently reflecting the current discourse
in this field and providing directions of future development. The
book is organized in six parts: towards AI transparency; methods
for interpreting AI systems; explaining the decisions of AI
systems; evaluating interpretability and explanations; applications
of explainable AI; and software for explainable AI.
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