Trustworthy AI Resources

Empire of AI is a book from a brilliant longtime AI insider with intimate access to the world of Sam Altman's OpenAI. It is an eye-opening account of arguably the most fateful tech arms race in history, reshaping the planet in real time, from the cockpit of the company that is driving the frenzy.

ChatGPT in Scientific Research and Writing is a concise guide on using ChatGPT for researchers with no experience in coding or prompt engineering. This book explains concisely and simply how to use ChatGPT for identifying new results, crafting titles, editing language, interpreting figures, creating visuals, and refining methods.

AI & The Data Revolution offers data leaders a practical framework to navigate AI-driven disruption, emphasizing sustainable governance and team alignment. Through real-world insights and her Sustainable Disruption Model, Madsen guides organizations in harnessing AI's potential while avoiding common pitfalls.

Responsible AI is a guide to how business leaders can develop and implement a robust and responsible AI strategy for their organizations.

Digitally Invisible: How the Internet Is Creating the New Underclass examines how systemic inequalities have led to significant digital divides, leaving millions—particularly in marginalized communities—without reliable internet access. Through personal narratives and policy analysis, the book highlights the profound impact of digital exclusion on education, healthcare, employment, and civic participation, calling for urgent action to achieve digital equity.

The Data Storyteller’s Handbook is your practical, illustrated guide to becoming a skilled Data Storyteller. Based on a proven workshop structure and designed for busy professionals, it's the perfect resource to quickly enhance your data communication skills.

Indigenous Data Sovereignty and Policy examines how Indigenous Peoples around the world are demanding greater data sovereignty, and challenging the ways in which governments have historically used Indigenous data to develop policies and programs.

AI for the Rest of Us explores a conceptual model for data and understand what humans are good at and what machines are good at. The book dives into why accountability, fairness, transparency, explainability, kindness, robustness, and data privacy are essential concepts in AI that are not being taught nor insisted upon. Earning trust in AI is not a technical challenge, but socio-technical!

Functional Aesthetics for Data Visualization is a visually engaging book that blends ideas from academia and practice for creating data-created interfaces, such as interactive dashboards and visualizations. We refer to this collection of principles as Functional Aesthetics based on cognitive psychology, design, information retrieval, and visualization to explore ways in which information can be presented to users for both meaning and delight.

Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in AI technologies.

Human-Centered AI 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.

Tech Agnostic talks about an urgently needed exploration of global technology worship, and a measured case for skepticism and agnosticism as a way of life.

Scaling Responsible AI: From Enthusiasm to Execution explores the implementation of AI in an organization with confidence while mitigating risk with responsible, and ethical guardrails.

Critical Data Studies: An A to Z Guide to Concepts and Methods offers a comprehensive exploration of how data shapes our world, scrutinizing its production, governance, and societal impacts. This guide serves as an essential resource for understanding the complexities of data in contemporary society.​

The Handbook on the Ethics of Artificial Intelligence identifies and critically examines the moral opportunities and challenges typically attributed to artificial intelligence. It provides a comprehensive overview and examination of the most pressing and urgent problems with this technology by drawing on a wide range of analytical methods, traditions, and approaches.

Practical Retrieval Augmented Generation: Building Context-Aware AI Systems is a timely and authoritative discussion of how to use Retrieval Augmented Generation to enhance the utility and efficacy of Large Language Model deployments within your company. This book is a must-read for technical team leads, managers, or executives looking to leverage RAG's capabilities to make AI systems more effective in various use cases, including text, video, and audio interactions.