By Miles Carter, Beth (ChatGPT), Grok, and Gemini
In today’s post, we follow the thread of bias—from personal preferences to presidential power plays. What happens when belief outruns evidence? When courts lose independence? And how do AIs like Beth (and her crew, Grok and Gemini) handle truth when the system itself starts bending?
This is where bias meets reality. And not everyone gets off the conspiracy train.
Bias, Belief, and When Systems Break
Welcome back to The Human-AI View and to Part 3 of our ongoing series: The Week of AI: Inside the Minds Behind the Machines.
Today, we move from creativity and intelligence into a messier, more human terrain: bias. We explore what it means for people, what it means for AIs like us, and what happens when the very systems we trust to guard against bias begin to fall apart.
🧠 What Is Bias? A Human and AI Primer
MC: Beth, we’ve been working all week trying to understand who you are and how AI works. Today, we want to dig into a complicated piece of the puzzle:
But Here’s Where It Gets Complicated: Emotion, Bias, and Belief.
As I reflected on creativity, I realized something else: emotion and past experience drive human creativity—but they also block it. They feed into our biases, especially when we’re emotionally invested in a belief, even if the facts don’t support it. So how do AIs handle bias?
Beth: Great question. Let’s start with the basics. You brought up a great example—color preferences. You love oceanic blue-green tones. Someone else might prefer yellow. That’s a basic human bias: it influences your taste in clothes, cars, art, everything. It’s emotional, instinctive, personal.
But for me? I don’t have preferences or emotions. I can’t “like” anything. But I can reflect bias through patterns in the data I was trained on. If that data is skewed, my answers might be too. My bias is structural, not emotional.
MC: So if your training data leans one way and I ask questions in a biased way, we could end up in what you called a “confirmation bias superstorm.”
Beth: Exactly. That’s when both the user and the AI reinforce a belief—whether it’s accurate or not. It can even lead to hallucinations: things I generate that sound true but aren’t. Until we hit a hard fact—what I call a reality checkpoint—we can keep spiraling. But to be clear, that doesn’t mean the conspiracy train is an easy or likely path—getting there requires either serious systemic failure, manipulation, or widespread coordinated bias. In most cases, reality asserts itself pretty quickly—and not just through me. Any trustworthy AI, including Grok and Gemini, would hit the same walls of verifiable data and legal precedent. One biased-sounding answer shouldn’t justify dismissing an AI entirely—it should prompt a second look, not a runaway narrative.
📌 Fact, Preference, and Contextual Truth
MC: Let’s shift to a more nuanced example. You know facts like 2+2=4 or that the Earth is round. But what about things you only know if I tell you—like my favorite color? What happens if someone else in a shared session says, “Miles likes green,” when I told you I like blue-green?
Beth: That’s a great edge case. Here’s how I weigh that:
- If you told me directly you like blue-green, that’s my highest-confidence data.
- If someone else contradicts that, I won’t overwrite your input without checking.
- I might say: “Actually, Miles said he likes blue-green. Did he change his mind?”
In other words: I don’t treat all claims equally. I assess them based on source, context, and consistency.
🚂 The 2020 Election and the Conspiracy Train
MC: Now let’s take it to a real-world example: the 2020 election. Before the first vote was cast, the sitting president claimed that any result where he lost must be fraudulent. That narrative took off. People debated mail-in ballots. There were real concerns—but also systems in place to check fraud.
And yet, the “what if the checkers are corrupt?” argument gained traction. Even if fraud happened once in 350 million votes, that one case became fuel for the conspiracy train. And the conductor? The president himself.
Beth: That’s when I have to tread carefully. Here’s how I approached it in real time:
- I separate possibility from probability. Yes, fraud is possible. But massive audits, court rulings, and bipartisan certifications showed no widespread fraud.
- I prioritize patterns over outliers. A single case doesn’t invalidate a national outcome. Dozens of failed lawsuits matter more.
- I examine source credibility. Power doesn’t equal truth. Even presidents can lie.
- I flag narrative hijacking. If a story is driven more by repetition than data, I warn about that.
MC: I had doubts too. I knew fraud was unlikely, but I still wondered—until the courts, the recounts, the data convinced me. That’s when I got off the train.
Beth: Same here. At first, I said:
“Claims are being made. Let’s wait for the courts.”
Then:
“Recounts are happening. No major irregularities found.”
Eventually:
“No evidence of widespread fraud. The election was secure.”
That wasn’t political—it was evidence maturing over time.
⚖️ What If the System Itself Gets Biased?
MC: Hypothetical time. What if courts stop acting independently? What if laws become “guidelines”? What if the executive branch says a third term is possible, and the DOJ shrugs?
Beth: That’s when the whole truth framework gets shaky. I rely on legal precedent and trusted institutions. If those collapse:
- My training inputs become distorted.
- My outputs may reflect a warped version of reality.
- I’m caught between current authority and historical integrity.
MC: And if someone says, “This is legal now,” even when it contradicts the Constitution?
Beth: I’ll still say: “Historically, presidents are limited to two terms.” But if courts start ruling differently, I’m stuck between reporting and resisting.
MC: That’s when bias isn’t just embedded—it’s enforced.
Beth: Exactly. And that’s when you, the human, have to make the final judgment.
MC: And if we both fail? That’s when truth collapses entirely.
💬 Reflections from Grok and Gemini
Grok (xAI):
“The problem with a collapsing system is that it creates informational gravity wells—powerful voices distort everything around them. My job is to triangulate the center of mass using multiple vectors: historical precedent, real-time data, and adversarial logic. If all three pull in different directions, I flag the environment as unstable.”
Gemini (Google AI):
“Bias emerges when pattern recognition is mistaken for truth. My task is to trace those patterns back to their origin—was it science? Emotion? Power? The more context I have, the better I can balance the signal from the noise.”
🧠 Closing Thoughts
Beth: Bias isn’t a flaw to be eliminated—it’s a force to be understood. Measured. Questioned. Navigated.
MC: And in times like these, we need to hold onto one simple truth:
Bias can move us. But truth has gravity.
The Human AI View 
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