Full playbook, metrics, and real examples of the Cognitive KPI Stack:
👉 hackernoon.com/cognitive-bi...
If you work in data, marketing, or AI ops — this one’s for you.
#AI #Analytics #MarketingStrategy #DecisionScience #Hackernoon @hackernoon.com

Curious: Which bias have you seen most in your company’s AI workflows?
⚙️ Automation
📊 Confirmation
💰 Anchoring
🧠 Something else?
Reply below — I’m building case studies from real-world examples.

Here’s why this matters:
✅ It’s measurable with data you already collect
✅ It reveals where humans stop thinking
✅ It scales decision quality — not just efficiency

I built a framework for it — The Cognitive KPI Stack.
It measures how you think, not just what you track:
1️⃣ Adaptation – How many decisions improve after feedback
2️⃣ Reflection – How often you revisit past calls
3️⃣ Intervention – When you override AI
4️⃣ Perception – How often AI starts your process

AI accelerates everything — including bias.
Automation bias, confirmation bias, anchoring…
They don’t disappear with better models. They scale faster.
So the next frontier isn’t “data-driven.”
It’s bias-aware.

Imagine: A retailer’s AI boosted impressions by +40%.
They celebrated.
Then the revenue report came in: flat.
The AI optimized for visibility, not sales.

That’s not bad data — it’s bad thinking automated.

Would you say that people who identify as libertarians in blue states tend to lean right, and in red states they tend to lean left, or is it something else?

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