Resources
Books
Human-Centered AI — Ben Shneiderman
The definitive academic text on designing AI systems that augment rather than replace humans. Shneiderman argues for a framework of reliable, safe, and trustworthy AI built around human agency and oversight. https://www.amazon.co.uk/dp/0198945345
Artificial Intelligence: A Guide for Thinking Humans — Melanie Mitchell
A clear-eyed, accessible overview of what AI can and can't do. Essential for grounding teams in the realities of the technology before they design with it. https://www.amazon.co.uk/dp/1250404851
The Alignment Problem — Brian Christian
Explores the challenge of getting AI systems to reflect human values and why that's harder than it sounds. Grounded in real examples from hiring algorithms to criminal sentencing tools. https://www.amazon.co.uk/dp/1786494337
Atlas of AI — Kate Crawford
A critical look at the material, political, and social costs of AI. Essential reading for any designer who wants to understand power dynamics and unintended consequences in AI systems. https://www.amazon.co.uk/dp/0300264631
Weapons of Math Destruction — Cathy O'Neil
How opaque, large-scale algorithms entrench inequality. A foundational book for understanding why human oversight and accountability in AI systems matter. https://www.amazon.co.uk/dp/0553418815
Papers
Explaining Explanations in AI — Mittelstadt, Russell, Wachter
A critical analysis of explainability in AI systems and why current approaches often fall short of what humans actually need. Core reading for anyone working on transparency. https://arxiv.org/pdf/1811.01439
Guidelines for Human-AI Interaction — Amershi et al. (Microsoft Research, 2019)
18 evidence-backed design guidelines derived from 20+ years of research, validated against 20 commercial AI products. One of the most practically useful papers in the field. https://dl.acm.org/doi/10.1145/3290605.3300233