A Developer S Guide To Building Ai Applications Pdf
Download A Developer S Guide To Building Ai Applications Pdf PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get A Developer S Guide To Building Ai Applications Pdf book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Human-Centered AI
Author: Ben Shneiderman
language: en
Publisher: Oxford University Press
Release Date: 2022-01-13
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
The Design of Human-Centered Artificial Intelligence for the Workplace
Author: Constantinos K. Coursaris
language: en
Publisher: Springer Nature
Release Date: 2025-08-02
Rapid advances in artificial intelligence (AI) are manifesting in increasingly sophisticated technologies and systems contributing to the digital transformation of organizations. These technological innovations involve the use of automation agents adding value through increased efficiency, effectiveness, service quality, and other performance-related dimensions. Motivated by the possibilities afforded by AI in organizational contexts of use, as well as by the challenges associated with AI, this book provides a comprehensive view of the considerations involved in designing AI-enabled systems, their application in the workplace, and the corresponding user experience. To this end, the book presents conceptual and empirical scientific perspectives on the design of human-centered AI, as well as case studies from multiple industries ranging from aerospace and automotive to retail, finance, and healthcare. These perspectives and evidence enable readers to consider and plan their own use cases for human-centered AI in the workplace. The book will be of interest to researchers and practitioners alike involved in the governance, design, development, implementation, and maintenance of human-AI-driven systems.
Auditing Artificial Intelligence
Artificial Intelligence (AI) is revolutionizing industries, yet its rapid evolution presents unprecedented challenges in governance, ethics, and security. Auditing Artificial Intelligence is an essential guide for IT auditors, information security experts, and risk management professionals seeking to understand, evaluate, and mitigate AI‐related risks. This book provides a structured framework for auditing AI systems, covering critical areas such as governance, compliance, algorithm transparency, ethical accountability, and system performance. With 24 insightful chapters, it explores topics including: AI Governance and Ethics – Establishing frameworks to ensure fairness, accountability, and transparency in AI deployments. Risk Management and Compliance – Addressing the legal and regulatory landscape, including GDPR, the EU AI Act, and ISO standards. Bias and Trustworthiness – Evaluating AI decision‐making to detect bias and ensure equitable outcomes. Security and Continuous Monitoring – Safeguarding AI systems from adversarial attacks and ensuring operational consistency. Model Performance and Explainability – Assessing AI outputs, refining accuracy, and ensuring alignment with business objectives. Designed for professionals tasked with assessing AI systems, this book combines practical methodologies, industry standards, and real‐world audit questions to help organizations build responsible and resilient AI practices and assess associated risks. Whether you are assessing AI governance, monitoring AI‐driven risks, or ensuring compliance with emerging regulations, this handbook provides the guidance you need to navigate and assess the complexities of AI systems with confidence. Stay ahead in your role and responsibility for assessing the rapidly evolving deployment and use of AI across the organization – equip yourself with the knowledge and tools to ensure its responsible, safe, approved, secure, and ethical use.