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From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), and be ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.
Master's Thesis from the year 2023 in the subject Computer Science - Commercial Information Technology, grade: 1,0, University of Regensburg (Professur für Wirtschaftsinformatik, insb. Internet Business & Digitale Soziale Medien), language: English, abstract: This thesis presents a toolkit of 17 user experience (UX) principles, which are categorized according to their relevance towards Explainable AI (XAI). The goal of Explainable AI has been widely associated in literature with dimensions of comprehensibility, usefulness, trust, and acceptance. Moreover, authors in academia postulate that research should rather focus on the development of holistic explanation interfaces instead of single visual explanations. Consequently, the focus of XAI research should be more on potential users and their needs, rather than purely technical aspects of XAI methods. Considering these three impediments, the author of this thesis derives the assumption to bring valuable insights from the research area of User Interface (UI) and User Experience design into XAI research. Basically, UX is concerned with the design and evaluation of pragmatic and hedonic aspects of a user's interaction with a system in some context. These principles are taken into account in the subsequent prototyping of a custom XAI system called Brain Tumor Assistant (BTA). Here, a pre-trained EfficientNetB0 is used as a Convolutional Neural Network that can divide x-ray images of a human brain into four classes with an overall accuracy of 98%. To generate factual explanations, Local Interpretable Model-agnostic Explanations are subsequently applied as an XAI method. The following evaluation of the BTA is based on the so-called User Experience Questionnaire (UEQ) according to Laugwitz et al. (2008), whereby single items of the questionnaire are adapted to the specific context of XAI. Quantitative data from a study with 50 participants in each control and treatment group is used to present a standardized way of quant
This picture book for kids talks about the interaction between Artificial Intelligence & humans.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
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.
**Unlock the Future with “Riding the Dragon”** Dive into the thrilling world of Artificial Intelligence and discover the transformative power of Human-AI partnerships with “Riding the Dragon.” This eBook is your comprehensive guide to understanding how AI is not just a tool, but a game-changer for industry, creativity, and daily life. **Revolutionize Your Perspective** “Riding the Dragon” begins with an introduction to Human-AI partnerships, tracing the fascinating evolution of AI technology. Embark on a journey through historical contexts and the latest advancements in autonomous systems and natural language processing. **Master Human-AI Collaboration** Explore the dynamic interplay between human skills and AI capabilities. From enhancing decision-making to fostering creativity and emotional intelligence, this eBook illustrates how AI can amplify human potential. Discover how AI is automating repetitive tasks, revolutionizing project management, and boosting communication and collaboration in the modern workplace. **Navigate Ethical and Psychological Dimensions** Unravel the complexities of data privacy, AI biases, and the imperative for accountability and transparency in AI use. Understand the psychological barriers, such as trust and job displacement fears, and learn strategies to embrace AI-driven change confidently. **Leverage AI for Personal and Professional Growth** “Riding the Dragon” showcases AI-driven personal productivity tools—from smart assistants to personalized health applications. Delve into real-world case studies in healthcare, finance, and education, illustrating the profound impact of successful Human-AI collaborations. **Stay Ahead with Regulatory Insights and Future Trends** Gain insights into international AI policies, industry-specific regulations, and future legislative trends. Stay ahead of the curve with predictions for the next decade, advancements in AI research, and the growth of AI startups. **Prepare for the AI-Ready Workforce** Equip yourself with the knowledge of essential skills, training programs, and the importance of lifelong learning. This eBook emphasizes cross-disciplinary knowledge integration, ensuring you are ready for the hybrid future of work. **Reflect, Innovate, and Thrive** Conclude your journey with reflections on Human-AI partnerships and practical guidance on innovating with AI tools and techniques. “Riding the Dragon” provides the encouragement and insights you need to prepare for an AI-augmented world and thrive in it. Embark on this enlightening exploration and position yourself at the forefront of the AI revolution with “Riding the Dragon.” Your future is now.
Providing a high level of autonomy for a human-machine team requires assumptions that address behavior and mutual trust. The performance of a human-machine team is maximized when the partnership provides mutual benefits that satisfy design rationales, balance of control, and the nature of autonomy. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a "collective intelligence. Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams focuses on human-machine trust and "assured performance and operation in order to realize the potential of autonomy. This book aims to take on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge the knowledge gaps, but also to advance this science to develop better solutions. - Assesses the latest research advances, engineering challenges, and the theoretical gaps surrounding the question of autonomy - Reviews the challenges of autonomy (e.g., trust, ethics, legalities, etc.), including gaps in the knowledge of the science - Offers a path forward to solutions - Investigates the value of trust by humans of HMTs, as well as the bidirectionality of trust, understanding how machines learn to trust their human teammates
With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.