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This book is written for software product teams that use AI to add
intelligent models to their products or are planning to use it. As
AI adoption grows, it is becoming important that all AI driven
products can demonstrate they are not introducing any bias to the
AI-based decisions they are making, as well as reducing any
pre-existing bias or discrimination. The responsibility to ensure
that the AI models are ethical and make responsible decisions does
not lie with the data scientists alone. The product owners and the
business analysts are as important in ensuring bias-free AI as the
data scientists on the team. This book addresses the part that
these roles play in building a fair, explainable and accountable
model, along with ensuring model and data privacy. Each chapter
covers the fundamentals for the topic and then goes deep into the
subject matter - providing the details that enable the business
analysts and the data scientists to implement these fundamentals.
AI research is one of the most active and growing areas of computer
science and statistics. This book includes an overview of the many
techniques that draw from the research or are created by combining
different research outputs. Some of the techniques from relevant
and popular libraries are covered, but deliberately not drawn very
heavily from as they are already well documented, and new research
is likely to replace some of it.
This book is written for software product teams that use AI to add
intelligent models to their products or are planning to use it. As
AI adoption grows, it is becoming important that all AI driven
products can demonstrate they are not introducing any bias to the
AI-based decisions they are making, as well as reducing any
pre-existing bias or discrimination. The responsibility to ensure
that the AI models are ethical and make responsible decisions does
not lie with the data scientists alone. The product owners and the
business analysts are as important in ensuring bias-free AI as the
data scientists on the team. This book addresses the part that
these roles play in building a fair, explainable and accountable
model, along with ensuring model and data privacy. Each chapter
covers the fundamentals for the topic and then goes deep into the
subject matter - providing the details that enable the business
analysts and the data scientists to implement these fundamentals.
AI research is one of the most active and growing areas of computer
science and statistics. This book includes an overview of the many
techniques that draw from the research or are created by combining
different research outputs. Some of the techniques from relevant
and popular libraries are covered, but deliberately not drawn very
heavily from as they are already well documented, and new research
is likely to replace some of it.
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