The Human-Centric AI That Build Trust
Jeriel Isaiah Layantara
CEO & Founder of Round Bytes
As an IT enthusiast, there are very few topics that fire up my attention as much as the relationship between human beings and artificial intelligence (AI). We’re long past the era when AI was relegated to mere novelty, and it has since found a permanent place in our digital lives, affecting everything from the search results we receive to the way we shop online. But here’s the million-dollar question: Is that really OK with us? Do we trust it? Better yet, do we get it?
That’s exactly what we’re getting into today, the art and science of Human-Centric AI. It’s not so much about making AI smarter as it is making it accessible, transparent and, in the end, trustworthy.
Consider this: most business owners with active websites are always on the lookout for an advantage. Tech geeks (like you and me) love the cutting edge of what’s new and newsworthy in tech. What connects us is an awakening realization that the future of AI isn n't as much about processing power as it is about user experience. If your A.I. doesn’t have the trust and comprehension of the user, all that computational genius is for nothing. Why “Human-Centric” Is No Longer Just a Buzzword
For most of A.I.’s modern history, development has focused on performance: How accurate can we make it? How fast could it learn? While the importance of these measures is beyond question, I believe a new and equal factor is at play: the humanity.
Think about what that means for your business. Say your e-commerce site employs A.I. to suggest items to you. If those recommendations seem random, or if the user can’t understand why a specific item was recommended, trust is undermined. It might lead them to ditch their cart, or to never return to the accessibility undermining this good work. Conversely, an AI that demonstrates its reasoning, even in a simple, elegant fashion helps create an environment of collaboration rather than mere automation.
This is a principle that goes far beyond E-commerce. Picture customer service chatbots that actually understand context, or AI-enabled analytics dashboards that not only spit out numbers, but offer actionable insights with explanations that make sense. This is the potential of human-centered AI.
The Foundation of Trust: Transparency and Interpretability
The “black box” perception is one of the largest barriers of adoption and acceptance of AI. We feed it data and it spits out an answer, but what happens in between is often opaque. This opacity breeds skepticism.
Here, transparency and interpretability are critical.
- Transparencyin AI is to understand what data AI is working with and how it is processed, and what are its limitations. It’s a little bit about pulling back the curtain, she said. For a business owner, this might mean feeling that the AI running your customer support is trained on anonymized chat logs, rather than that it’s pulling answers from the sky.
- Interpretability, however, is the ability to understand why an AI made a certain decision or prediction. It’s not sufficient for a credit risk assessment AI to reject a loan, the humanistic approach requires it to explain why it made the denial. Was it the candidate’s credit? Their debt-to-income ratio? Such a context gives the user and the decision maker authority.
Consider it this way: if a human expert told you something, you would probably ask them why. Why should Spector and his tool be any different? When the AI can explain itself, even just a bit, it goes from inscrutable oracle to helpful assistant.
Designing for Understanding: User Interface (UI) and User Experience (UX) for AI
Here is where the rubber hits the road. And yet one thing is to have a transparent and interpretable AI model, another is to make it come alive in an intuitive and understandable user interface. This is not about looking pretty, it’s about making complex AI interactions feel natural and straightforward.
Here are the good UI/UX principles applied to this new breed of UX:
- Feedback: Is feedback provided to the user when they interact with an AI? How does a chatbot handle a query it can’t make heads or tails of: does it merely parrot back what it was just told, or does it make helpful suggestions, explain its limitations or even smoothly escalate to a human agent? The latter is human-centric.
- Controllability and Agency: A user should feel as if they can influence the AI in some way, ideally even if it’s only to correct a mistake or add more context. Think, again, of image recognition AI, in which you can correct mislabeled objects being recognized — that sense of agency is my trust-builder.
- Correct Information Granularity It is very important. We don’t want AI interfaces to overload users with technical protocols they don’t care about. Instead, give them info that will serve them at the point where they are. A simple “Here’s why we think you’ll like this” often works better than an explanation of neural network weights.
- Consistent and Predictable: As with any well-designed system, AI interfaces need to be predictable. It is by interacting that users learn, and well-timed responses help build a mental model of the AI, which does not frustrate and confuses with its changing behavior.
The Art of Explanations: Making Complex AI Simple
And this is where the storytelling comes in. As an IT geek I love how machine learning algorithms are complex. But most users don't. Our role as designers and communicators, after all, is to take that complexity and turn it into narratives that people can understand.
Picture an A.I. that assists in diagnosing potential problems with your home network. Rather than simply stating, “Network error,” a human-centered AI could share: “It appears that your internet provider’s connection in your area is having an issue. That’s resulting in occasional signal drops on your Wi-Fi.” See the difference? It's informative, actionable, and easy to understand.
This involves:
- Analogize: Explain complex AI concepts through concrete, real-world examples that people can relate to.
- Visualizations: Charts, graphs and interactive features often tell a story more effectively than a thousand words of text.
- Step-by-step reasoning: Decompose the AI’s reasoning into pieces that can be more easily understood.
- Plain language: Avoid jargon. If you must use a technical term, please make the definition plain and brief.
The aim is not to dumb AI down; it is to make its intelligence transparent.
