Human Centered Ai

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.

Artificial intelligence (AI) permeates our lives in a growing number of ways. Relying solely on traditional, technology-driven approaches won't suffice to develop and deploy that technology in a way that truly enhances human experience. A new concept is desperately needed to reach that goal. That concept is Human-Centered AI (HCAI). With 29 captivating chapters, this book delves deep into the realm of HCAI. In Section I, it demystifies HCAI, exploring cutting-edge trends and approaches in its study, including the moral landscape of Large Language Models. Section II looks at how HCAI is viewed in different institutions—like the justice system, health system, and higher education—and how it could affect them. It examines how crafting HCAI could lead to better work. Section III offers practical insights and successful strategies to transform HCAI from theory to reality, for example, studying how using regulatory sandboxes could ensure the development of age-appropriate AI for kids. Finally, decision-makers and practitioners provide invaluable perspectives throughout the book, showcasing the real-world significance of its articles beyond academia. Authored by experts from a variety of backgrounds, sectors, disciplines, and countries, this engaging book offers a fascinating exploration of Human-Centered AI. Whether you're new to the subject or not, a decision-maker, a practitioner or simply an AI user, this book will help you gain a better understanding of HCAI's impact on our societies, and of why and how AI should really be developed and deployed in a human-centered future.

Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

Design Anthropology provides the definitive introduction to the field of design anthropology and the concepts, methods, practices and challenges of this exciting and emerging area of study

"The most important book on AI this year." --The Guardian "Mr. Russell's exciting book goes deep, while sparkling with dry witticisms." --The Wall Street Journal "The most important book I have read in quite some time" (Daniel Kahneman); "A must-read" (Max Tegmark); "The book we've all been waiting for" (Sam Harris) A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable. In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage. If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial.

Technology advances are making tech more . . . human. This changes everything you thought you knew about innovation and strategy. In their groundbreaking book, Human + Machine, Accenture technology leaders Paul R. Daugherty and H. James Wilson showed how leading organizations use the power of human-machine collaboration to transform their processes and their bottom lines. Now, as new AI powered technologies like the metaverse, natural language processing, and digital twins begin to rapidly impact both life and work, those companies and other pioneers across industries are tipping the balance even more strikingly toward the human side with technology-led strategy that is reshaping the very nature of innovation. In Radically Human, Daugherty and Wilson show this profound shift, fast-forwarded by the pandemic, toward more human—and more humane—technology. Artificial intelligence is becoming less artificial and more intelligent. Instead of data-hungry approaches to AI, innovators are pursuing data-efficient approaches that enable machines to learn as humans do. Instead of replacing workers with machines, they're unleashing human expertise to create human-centered AI. In place of lumbering legacy IT systems, they're building cloud-first IT architectures able to continuously adapt to a world of billions of connected devices. And they're pursuing strategies that will take their place alongside classic, winning business formulas like disruptive innovation. These against-the-grain approaches to the basic building blocks of business—Intelligence, Data, Expertise, Architecture, and Strategy (IDEAS)—are transforming competition. Industrial giants and startups alike are drawing on this radically human IDEAS framework to create new business models, optimize post-pandemic approaches to work and talent, rebuild trust with their stakeholders, and show the way toward a sustainable future. With compelling insights and fresh examples from a variety of industries, Radically Human will forever change the way you think about, practice, and win with innovation.

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.

This book constitutes the refereed proceedings of the Second International Conference on Artificial Intelligence in HCI, AI-HCI 2021, which was held as part of HCI International 2021 and took place virtually during July 24-29, 2021. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers included in this volume were organized in topical sections as follows: Ethics, trust and explainability; human-centered AI; AI applications in HCI; and AI applications in smart environments.