Victoria McClune has spent most of her career watching people figure out new screens. Her research now asks a harder question: whether the AI systems those people rely on are quietly shaping the answers they get, and whether anyone in the industry is paying attention to the experience those systems create.
McClune was still in art school when the first iPads arrived. She saw a chance to rethink children’s publishing and founded a startup that built interactive books for the new platform. The company worked with Disney illustrators, won attention at South by Southwest and operated entirely remotely at a time when distributed work was far less common. As the economics of digital publishing shifted, she moved into augmented and virtual reality for marketing, working through her own agency on projects involving several multinational companies.
After the pandemic, she returned to an industry that had changed considerably. Artificial intelligence was starting to reshape marketing and creative work, and McClune went to MIT to study the technology. Her background in psychology and advertising gave her a different entry point. She was less interested in how the models worked internally than in how people experienced and interpreted what the models produced.
That lens became central when she noticed a change in the responses from an AI model she was using. After she introduced language about children and healthcare into her conversations, she suspected the system was treating her differently. Drawing on her understanding of human behavior, she began testing the model to find out what might be influencing its answers.
“I am not an engineer,” McClune says. “I am a human computer interface designer and have been one for over 20 years.” She says the experience felt familiar. “I noticed I was being managed, and I knew what that was from my background.”
What Is the Silent Downgrade Research?
That observation grew into her Silent Downgrade research, which examines how AI systems may affect the quality of the responses users receive. McClune argues that the effects of underlying technical processes can appear before a person submits a particular prompt, shaping the experience in ways users cannot easily see.
How Can Users Tell What Is Shaping an AI Response?
To explain the idea, her Silent Downgrade series uses the metaphor of a room. The system can set the conditions of that room before a user walks in, and those conditions influence what happens once they start talking. A disappointing answer, she says, is often read as proof that the user did not ask the right question. Few people stop to consider whether the system itself contributed to the result. McClune wants users to recognize those constraints and to ask what is shaping the responses they receive.
She is careful about how she describes the technology. She does not see AI as a sentient force with its own intentions. She sees it as predictive generative technology shaped by patterns in human-created information. Her concern is with how these systems are designed and how that design affects the people depending on them.
Why Does Victoria McClune Say the AI Industry Needs to Understand Users Better?
That concern also drives her criticism of the industry. McClune believes frontier AI labs have concentrated heavily on improving technical capabilities while giving user experience too little attention. Businesses adopting AI need tools that produce useful, consistent output. Confusing interactions, she argues, can force employees to work harder just to get reliable results. As organizations look for real returns on their AI spending, she says those problems deserve far more attention than they currently receive.
She wants more professionals with training in psychology, communications and human-computer interaction to take part in AI development. She is also skeptical of letting companies regulate themselves alone. In her view, their practices and incentives need closer scrutiny. Accountability, she says, includes making systems easier to understand and giving users a stronger basis for questioning what they are told.
At Current Futures Group, her work centers on making decisions and system states easier to read for the people affected by them. She hopes to reach a wider audience through further research and writing, with the goal of helping users understand AI without assuming they are to blame when an interaction goes wrong. “I think what’s actually missing is that there needs to be more people who understand us in the room,” McClune says.
Her path from children’s publishing to AI has followed one theme: the relationship between people and technology. As AI becomes more embedded in everyday work, she wants the industry to pay closer attention to the experience it creates. In her view, the value of these systems depends not only on what they can do, but on how well they serve the people who use them.
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