I was trying to get a professional headshot the other day. You know how it is. I needed something recent, and all my decent photos are either me in the garden covered in dirt or family shots with my daughter.
There was this one photo from Mexico City I really liked. Good lighting, I actually looked awake, my hair was doing what it was supposed to. Only problem? Aldo had his arm around me. Not exactly the solo professional headshot I needed.
So I turned to Photoshop’s new AI features. Simple request, I thought. Remove the person next to me, give me a neutral professional background. What could go wrong?
The AI looked at my photo, understood the assignment, and promptly… gave me a new boyfriend.
Not a modified Aldo. Not an empty space where Aldo used to be. A completely different Hispanic-looking man, arm still around me, same intimate couple pose. The AI had racially profiled my actual partner just enough to select an appropriate replacement from its training data. Like it was saying, “Based on the statistical probability of who this woman would have her arm around, let me provide you with Hispanic Male, Option B.”
I laughed until I nearly cried. Then I tried again.
Second attempt? The AI removed Aldo successfully this time, but decided I needed a jungle background and put a cocktail in my hand. Because nothing says “professional headshot” like sipping a mojito in the rainforest, apparently.
This whole experience reminded me of another trend that swept through LinkedIn a while back: asking ChatGPT to create an image of you based on what it knows from your conversations. I tried it, curious what patterns the AI had picked up about me.
First result: I was a white man with a beard, slight smile. The classic “software developer” stereotype from every stock photo ever taken.
I tried again a few weeks later, after they’d clearly done some diversity training on their models. This time? I was a Black woman with natural hair and glasses, standing on a generic city street.
The overcorrection was almost funnier than the original bias. Like watching someone try so hard not to be racist that they circle back around to being weird about race in a completely different way.
Here’s what fascinates me about all this: these systems are doing exactly what they’re trained to do. When my arm was positioned like it was around someone, the AI couldn’t comprehend that I wanted that someone to not exist. Its training data says arms in that position belong around people. So it provided a person.
When asked to imagine what a software developer named Anna looks like, it ping-ponged between “definitely a white man” and “we’ve been told to increase diversity, so definitely not a white man”, never quite landing on anything close, despite a lengthy chat and project history which knows so much about me.
The problem isn’t that these models are broken. They’re pattern-matching perfectly against their training data. The problem is they have no context for what we actually want or who we actually are. They’re making statistical guesses based on millions of images and conversations that may or may not represent reality, and definitely don’t represent individual reality.
This is exactly why I’ve been obsessed with Model Context Protocol (MCP) lately. It’s attempting to solve this exact problem: how do we give AI systems the context they need to understand not just the statistical average, but the specific situation? How do we move from “woman with Hispanic partner probably wants another Hispanic man in photo” to “this particular person wants this particular other person removed from this particular photo”?
Context isn’t just about providing more information. It’s about providing the right information at the right time. It’s the difference between an AI that replaces your boyfriend with a statistical probability and one that understands what you’re actually trying to achieve.
Third time was the charm, by the way. Finally got that professional headshot with a normal background. No replacement boyfriends, no tropical cocktails. Just me, looking professionally adequate, ready for my speaker bio.
Though I’m keeping the jungle cocktail version. You never know when you’ll need a professional photo that says “I debug JavaScript from the rainforest.”
I’ll be diving deeper into MCP and how context shapes AI behavior at Web Directions Developer Summit in Sydney this November. If you’re curious about the technical side of why AI keeps making these hilarious (and sometimes concerning) assumptions, come find me there.
ps. what do you think of my headshot now?
FAQ
Why did AI give me a different boyfriend instead of removing him?
AI image models are trained on statistical patterns, not intent. When your arm position matched “couple pose” in training data, the model filled the gap with the most statistically likely person — rather than understanding you wanted an empty space.
What is Model Context Protocol (MCP) and why does it help?
MCP is an open standard that lets AI systems receive structured context about a specific situation, rather than guessing from statistical averages. It’s the difference between AI that replaces your partner with “Hispanic Male, Option B” and AI that understands exactly what you’re trying to achieve.
Does AI have racial bias in image generation?
Yes — current models reflect biases in their training data. The pattern of replacing Aldo with a “statistically probable” partner, and generating a white male developer as a default, are both examples of bias baked into training data rather than intentional design.
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