The systems around you are not just predicting your clicks; they are forming theories about your mind. This book shows, in plain language, how those theories are made, how they shape what you see next, and how to take back the driver's seat. It explains how modern models infer goals and shortcuts from your every action, why those inferences can become self-fulfilling, and what it takes to interrupt harmful loops. Drawing on cognitive science, product reality, and lived examples, it gives you a field guide to read any major AI system as a claim about you. If you have ever wondered why your feed narrows, why outrage travels faster than nuance, or how to make better-than-default choices, this is your map. - Understand AI and human behaviour without hype or panic - Spot cognitive bias in algorithms and how your own habits train it - Practise data psychology to manage what systems learn about you - Break algorithmic feedback loops with simple, durable routines - Build predictive models and agency that work in your favour - Use explainable AI for users to decode outputs as hypotheses - Reclaim your recommender systems and choice with deliberate exploration - Safeguard digital identity and reputation scores with practical audits - Connect neuroscience and machine learning to everyday decisions - See exactly how AI learns from data and how to teach it better things By the end, you will hold a clear mental model for reading predictions, resetting patterns, and steering systems that are learning you. Not by opting out, but by opting in with craft.