HumanPrint: How to Use AI Without Losing Yourself
Jul 2, 2026 · 10 min · 8 segments
## **What this episode is about** ## Each data point represents one pixel of the human experience. That's a stats idea, but it's also a writing idea — the word is the pixel, and the human experience…
Christine WhitmarshHostToday, I want to start with something I wrote in an article about data storytelling.
It's one of my passions because it's the idea that this whole episode today is built around.
And that idea is each data point represents one pixel of the human experience.
Now in statistics, a data point is a number, but that number that comes from somewhere, it comes from a person answering a question or making a choice, experiencing a symptom or a behavior, buying something, leaving a job, showing up, not showing up.
That number is the record, but the human experience is what generated it.
And if you're only looking at the trend line, only looking at the pattern in aggregate, you can miss what's actually happening at the individual human level.
So AI can produce words, a lot of them, fluently, convincingly, but without the human experience underneath them, which means the pixels are there, but the picture might not be.
It's one of those things, you know, so one of the things I came out of that program with was the ability to look at a scatterplot and kind of hold two ideas at once, the trend line and the individual dots.
The trend is exciting, you know, patterns and data are exciting, but so is the actual scatter of the dots, because every dot in that scatter plot is a person with an experience.
And sometimes the most important thing in the data set isn't the trend at all, it's the outlier, the dot that doesn't follow the pattern, the survey response that makes you go, wait, hold on, why did this person answer this way? What are they telling us that the trend can't? So when I was running my scoliosis pain and exercise pilot study, it was my master's thesis.
It's on my website, christinewhitmarsh.com. You know, the trends were genuinely exciting to me.
The exploratory factor analysis, the pain variables that held it all together, but equally exciting were the individual survey responses because reading through them, I was seeing my own fellow scoliosis survivors, people who share a condition that I live with in my own body, and every single response was a pixel of a human experience that I recognized.
And I think that's what human judgment does that pattern recognition alone, like AI, cannot.
Today, I want to start with something I wrote in an article about data storytelling.
It's one of my passions because it's the idea that this whole episode today is built around.
And that idea is each data point represents one pixel of the human experience.
Now in statistics, a data point is a number, but that number that comes from somewhere, it comes from a person answering a question or making a choice, experiencing a symptom or a behavior, buying something, leaving a job, showing up, not showing up.
That number is the record, but the human experience is what generated it.
And if you're only looking at the trend line, only looking at the pattern in aggregate, you can miss what's actually happening at the individual human level.
So AI can produce words, a lot of them, fluently, convincingly, but without the human experience underneath them, which means the pixels are there, but the picture might not be.
It's one of those things, you know, so one of the things I came out of that program with was the ability to look at a scatterplot and kind of hold two ideas at once, the trend line and the individual dots.
The trend is exciting, you know, patterns and data are exciting, but so is the actual scatter of the dots, because every dot in that scatter plot is a person with an experience.
And sometimes the most important thing in the data set isn't the trend at all, it's the outlier, the dot that doesn't follow the pattern, the survey response that makes you go, wait, hold on, why did this person answer this way? What are they telling us that the trend can't? So when I was running my scoliosis pain and exercise pilot study, it was my master's thesis.
It's on my website, christinewhitmarsh.com. You know, the trends were genuinely exciting to me.
The exploratory factor analysis, the pain variables that held it all together, but equally exciting were the individual survey responses because reading through them, I was seeing my own fellow scoliosis survivors, people who share a condition that I live with in my own body, and every single response was a pixel of a human experience that I recognized.
And I think that's what human judgment does that pattern recognition alone, like AI, cannot.
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