Designing Human-Centric AI Experiences: A Book Guide

If you build products that touch AI in any way, you've probably felt the tension between what a model can do and what a person actually needs. Designing human-centric ai experiences is the phrase Akshay Kore uses to name that tension, and his book of the same title has become a reference point for UX designers, product managers, and researchers trying to keep humans at the center of AI-driven design decisions.

This guide breaks down what the book actually covers, who Kore wrote it for, and whether it delivers on its promise. You'll get a plain look at the core frameworks, the practical exercises, and the real critiques from designers who've read it cover to cover, plus where to buy it and in what formats.

We put this together because the questions Kore raises, consent, trust, and human agency in AI systems, are the same ones driving our work at the Human Authenticity Registry. Whether you're designing an AI product or just trying to stay human-legible inside one, this book gives you a working vocabulary worth knowing.

Why human-centered design matters in the age of AI

The trust gap AI keeps opening

Every time a chatbot pretends to be a person, or a synthetic voice reads a script no one signed off on, users lose a little more trust in what they're seeing and hearing online. Kore's central claim is that AI systems fail people not because the underlying models are broken, but because teams skip the step of asking who the technology actually serves. That gap between capability and care is exactly what human-centered AI design is meant to close, and it's why the phrase shows up in so many product roadmaps right now, even when teams don't fully agree on what it means in practice.

When a product can't tell a user what's real, it's already lost the only thing worth designing for: trust.

Where the breakdown actually happens

Several recurring failure points show up across the AI products Kore studies, and they map closely to complaints we hear from HAR members who've watched their likeness or voice get imitated without consent:

• Disclosure gaps: users can't tell when they're talking to a model versus a human

• Consent shortcuts: data or likeness gets used in ways nobody explicitly agreed to

• Opaque defaults: settings that quietly favor the company's data needs over the user's control

• Missing recourse: no clear path to correct, delete, or dispute an AI-generated record

None of these require exotic technical fixes. They require someone in the room asking the human question before the engineering question.

Why this isn't just a UX nicety

Researchers who study AI ethics in design, and especially those working on human-centered explainable AI, increasingly treat these questions as safety issues, not polish. The Federal Trade Commission has already signaled it will treat deceptive AI-generated content as a consumer protection matter, which raises the stakes for any team shipping a product that can impersonate a real person. Skipping the human-centered layer isn't a style choice anymore; it's a liability. That's also the practical argument for registries and consent frameworks like HAR: they give people a documented, verifiable way to say "this is really me," which is precisely the missing piece Kore keeps circling back to throughout the book.

How to apply the book's principles to your own AI work

Start with a disclosure audit

Before you touch a design system, run a simple audit of every place your product speaks, listens, or generates on a user's behalf. Kore's framework asks you to map each AI touchpoint against a plain question: does the person on the other end know what they're dealing with? Teams that skip this step end up retrofitting consent language after launch, which is slower and costlier than building it in from the start. This is the same audit logic behind HAR's own proof of humanity approach, where disclosure is baked into the registration flow rather than bolted on later.

If a user has to guess whether they're talking to a machine, you've already failed the first design principle.

Turn principles into a checklist

Reading the book without translating it into a working checklist wastes half its value. Pull these into your next design review:

• Label AI-generated content at the point of contact, not buried in settings

• Give users a working opt-out path for data or likeness use

• Build a visible recourse mechanism for disputing or deleting a synthetic record

• Test defaults with people outside the design team, not just internal reviewers

Applying human-centric AI design principles this way turns Kore's ideas from theory into something your engineering team can actually ship against, sprint by sprint.

What's inside: key chapters and topics covered

How the book is organized

Kore structures the book around a progression: first he defines the problem of trust erosion, then walks through frameworks for disclosure, consent, and control, and closes with case studies from chatbots, voice assistants, and generative image tools. Each chapter pairs a short conceptual argument with a worksheet you can run in a real design sprint, which is why so many readers treat it as a working manual rather than a theory text.

Chapter breakdown at a glance

Readers consistently point to the Consent by Design chapter as the most cited section in reviews, likely because it hands designers language they can lift straight into a product requirements doc. The Data and Likeness chapter runs closest to HAR's own territory, walking through the same questions our members ask about who controls a face or voice once it's been captured digitally. Skimmers should still read the case studies in full; that's where AI experience design stops being abstract and starts looking like the products you already use every day.

Where to get the book and who should read it

Formats and where to buy

Akshay Kore self-published the book, so you won't find it stacked at a big-box retailer. Most readers pick it up as a paperback or ebook through major online retailers like Amazon, and a Kindle edition is available for anyone who prefers reading design references on a tablet during a commute. Pricing sits in the same range as most niche UX titles, roughly what you'd pay for a single conference workshop, which makes it an easy purchase for a design team's book budget rather than an individual splurge.

A book this practical earns its keep the first time it saves you from shipping a consent flow nobody asked for.

Who actually needs this on their shelf

Beyond individual designers, teams building anything with a synthetic voice, face, or chat interface should treat this as required reading, not optional inspiration.

• UX designers and researchers building conversational or generative products

• Product managers who need a shared vocabulary for consent and disclosure debates

• Trust and safety teams drafting policy for AI-generated content

• Founders at early-stage AI startups who haven't yet hired a dedicated design lead

Surveys of AI-driven product teams show most disclosure complaints trace back to decisions made before a designer was ever in the room, which is exactly the gap designing human-centric ai experiences as a discipline tries to close before launch, not after a public backlash forces a rewrite.

A final word on designing AI for people

Kore's book won't hand you a finished product, but it hands you the right questions before you build one. Designing human-centric ai experiences comes down to one habit: asking who a feature serves before you ship it, then giving that person a real way to say yes, no, or dispute what's happened to their likeness. Teams that skip this step end up patching consent language after a backlash, which costs more than building it in from day one.

You don't have to wait for a redesign to put these ideas into practice. Consent and disclosure are things you can act on right now, as an individual, not just as a design team. If you want a concrete way to assert that your face and voice are genuinely yours before an AI system claims otherwise, put your consent on record with the Human Authenticity Registry.