Author Description

Bias in AI and Machine Learning" digs deep into the often-unseen world of algorithmic bias, a subtle yet significant issue affecting artificial intelligence (AI) and machine learning (ML) systems. Starting with a primer on AI and ML, the book elucidates their growing impact on society and the omnipresence of these technologies in our daily lives. The core concept of algorithmic bias is introduced, outlining how and why it affects machine learning models. The heart of the book examines various types of biases such as representation, sensor, demographic, temporal, geographical, and evaluation bias. Each bias type is elucidated through detailed mathematical and statistical models, allowing readers to grasp these biases and their implications quantitatively. For instance, the book profoundly explains representation bias using discrepancy functions and the total variation distance. Two of its standout explorations include housing pricing and face detection algorithms, showcasing real-world scenarios of biases and their ramifications. Moreover, it takes a compelling dive into dating algorithms, shedding light on implicit biases and their impact on digital romance. Through meticulously crafted chapters, readers will not only grasp the depth of biases inherent in AI but also learn actionable strategies for their detection and mitigation. An essential read for anyone seeking to understand the interplay between technology, society, and fairness in the modern digital landscape. Its final chapter, "Bias in AI and Machine Learning," offers practical strategies to mitigate these biases. It presents methods for gathering diverse and representative data, implementing robust evaluation metrics, and continuously testing and refining AI models. The book also reflects on the ethical dimensions of AI and ML, emphasizing the need for transparency, accountability, and fairness in their use. A crucial resource for students, practitioners, and anyone inte