Ai And Ux

As venture capital and industrial resources are increasingly poured into rapid advances in artificial intelligence, the actual usage and success of AI depends on a satisfactory experience for the user. UX will play a significant role in the adoption of AI technologies across markets, and AI and UX explores just what these demands will entail. Great effort has been put forth to continuously make AI “smarter.” But, will smarter always equal more successful AI? It is not just about getting a product to market, but about getting the product into a user’s hands in a form that will be embraced. This demands examining the product from the perspective of the user. Authors Gavin Lew and Robert Schumacher have written AI and UX to examine just how product managers and designers can best strike this balance. From exploring the history of the parallel journeys of AI and UX, to investigating past product examples and failures, to practical expert knowledge on how to best execute a positive user experience, AI and UX examines all angles of how AI can best be developed within a UX framework. The new world of AI necessitates an equally new UX lens through which to see all potential products. While massive inroads have created strides in AI technology, it must be accessible and easy to use for the consumer. Innovators in the field need to shift thinking from “it works” to “it works well,” which makes all the difference in increasing adoption. Let your users enhance your data, and let the UX of your product do the selling for you. AI and UX is your roadmap for the future. What You'll Learn Understand how the usage and success of AI depends on a great user experience Discover how technology can advance beyond “it works” to “it works well,” which subsequently increases its adoption Determine what ways can we let the users enhance the data to make AI better attuned to their needs Realize how you can make humans smarter in their interactions with AI Who This Book Is For Those interested in AI and future implications; these can be futurists, technophiles, or product designers and product managers working on AI products

User experience (UX) design practices have seen a fundamental shift as more and more software products incorporate machine learning (ML) components and artificial intelligence (AI) algorithms at their core. This book will probe into UX design’s role in making technologies inclusive and enabling user collaboration with AI. AI/ML-based systems have changed the way of traditional UX design. Instead of programming a method to do a specific action, creators of these systems provide data and nurture them to curate outcomes based on inputs. These systems are dynamic and while AI systems change over time, their user experience, in many cases, does not adapt to this dynamic nature. Applied UX Design for Artificial Intelligence will explore this problem, addressing the challenges and opportunities in UX design for AI/ML systems, look at best practices for designers, managers, and product creators and showcase how individuals from a non-technical background can collaborate effectively with AI and Machine learning teams. You Will Learn: Best practices in UX design when building human-centric AI products or features Ability to spot opportunities for applying AI in their organizations Advantages and limitations of AI when building software products Ability to collaborate and communicate effectively with AI/ML tech teams • UX design for different modalities (voice, speech, text, etc.) Designing ethical AI system

Advances in narrow artificial intelligence make possible agentive systems that do things directly for their users (like, say, an automatic pet feeder). They deliver on the promise of user-centered design, but present fresh challenges in understanding their unique promises and pitfalls. Designing Agentive Technology provides both a conceptual grounding and practical advice to unlock agentive technology’s massive potential.

Master's Thesis from the year 2023 in the subject Computer Science - SEO, Search Engine Optimization, 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 quantifying the dimensions of Usability and UX specifically for XAI systems. Furthermore, through an A/B test, evidence is presented that visual explanations have a significant (α=0.05) positive effect on the dimensions of attractiveness, usefulness, controllability, and trustworthiness. In summary, this thesis proves that explanations in computer vision not only have a significantly positive effect on trustworthiness, but also on other dimensions.

This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions—offering a compelling, necessary read for our ChatGPT era. Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions. Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein’s monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.

The User Experience Team of One prescribes a range of approaches that have big impact and take less time and fewer resources than the standard lineup of UX deliverables. Whether you want to cross over into user experience or you're a seasoned practitioner trying to drag your organization forward, this book gives you tools and insight for doing more with less.

Are you ready to unlock the potential of AI through exceptional user experience design? "UX for AI" demystifies the complex world of user experience in the burgeoning field of artificial intelligence. This book is crafted for beginners and provides a thorough introduction to the essentials of UX, tailored specifically for AI applications. In this guide, you'll discover: Fundamentals of UX: Learn what UX is, how it differs from UX research, and why it's crucial in AI. History and Theory: Explore the evolution of UX design thinking and how psychological principles apply to user-centered design. Practical Approaches: Gain insights into defining design problems, conducting effective research, and employing AI-specific design strategies. Ethics and Accessibility: Tackle critical issues such as ethical design, bias in AI, and creating inclusive experiences. Career Guidance: Understand the landscape for UX careers in AI, including skills needed and potential career paths. Whether you're a budding designer, a tech professional looking to shift into UX, or simply AI-curious, "UX for AI" offers the tools and knowledge you need to create impactful, user-friendly AI systems. Embark on your UX journey today and start designing the future of AI!

Proceedings of the 10th International Conference on Human Interaction and Emerging Technologies, IHIET 2023, August 22-24, 2023, Université Côte d'Azur, Nice, France.