Electronic Books on AI Bias
AI Bias Electronic Books
Explore our curated collection of electronic books on artificial intelligence bias. These titles examine how algorithms can reflect and reinforce societal inequities, offering insights into ethical considerations, data integrity, and strategies for creating fair and transparent AI systems.
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Artificial Intelligence and Digital Diversity Inclusiveness in Corporate Restructuring
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In this era of globalization, diversity in the business environment is about more than gender, race and ethnicity. It now includes employees with diverse religious and political beliefs, education, socioeconomic backgrounds, sexual orientation, cultures and even disabilities. Companies are discovering that, by supporting and promoting a diverse and inclusive workplace, they are gaining benefits that go beyond the optics. There is no industry left where artificial intelligence is not used in some capacity. Considering the pace at which AI, machine learning and deep learning have been developing in recent years, it is expected that AI will profoundly modify the way we live and work. One of the areas in which this paradigm may stand out in the future is related todiversity and inclusionwithin companies. AI can detect potential bias and prejudice in decision-making by simulating intelligent behaviour, potentially reducing trends and prejudices that could hinder organizations' ability to recruit diversely and inclusively.
Call Number: Electronic BookISBN: 9781685077860Publication Date: 2022-07-27 -
Ethics in Artificial Intelligence: Bias, Fairness and Beyond
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This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments - the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in softwaretesting/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.
Call Number: Electronic BookISBN: 9789819971862Publication Date: 2025-01-04 -
Hands-On Artificial Intelligence for Beginners
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Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease Key Features Enter the world of AI with the help of solid concepts and real-world use cases Explore AI components to build real-world automated intelligence Become well versed with machine learning and deep learning concepts Book Description Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications. What you will learn Use TensorFlow packages to create AI systems Build feedforward, convolutional, and recurrent neural networks Implement generative models for text generation Build reinforcement learning algorithms to play games Assemble RNNs, CNNs, and decoders to create an intelligent assistant Utilize RNNs to predict stock market behavior Create and scale training pipelines and deployment architectures for AI systems Who this book is for This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.
Call Number: Electronic BookISBN: 9781788991063Publication Date: 2018-10-31 -
Dominant Algorithms to Evaluate Artificial Intelligence: from the View of Throughput Model
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No detailed description available for "Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model".
Call Number: Electronic BookISBN: 9789815049541Publication Date: 2022-08-03
