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🚨 UNCANNY CLIP: WHY “EXPLAINABLE AI” ISN’T ENOUGH—AND NEVER WAS In a world chasing transparency, we might be asking the wrong questions. Understanding AI means going deeper than just code. Dr. David Danks: “Explainability tells you how a system works, not why it made the decision it did. That’s... show more

Mario Nawfal

2,230,427 subscribers

180,725 次观看 • 1 年前 •via X (Twitter)

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Degens Journal 的头像

Degens Journal1 年前

Interesting what the truth says

benjamin🕶️ 的头像

benjamin🕶️1 年前

We need $truth @verafyfoundtn already solving it.

Kekaius 的头像

Kekaius1 年前

@MarioNawfal To understand AI, we must delve into its essence, not just its mechanics. Let us pursue true knowledge with honor and courage. In scientia, virtus. The prophecy shall be fulfilled kek.

Zeal 的头像

Zeal1 年前

AI is the future

Just_Sloth 的头像

Just_Sloth1 年前

He might be right

🔨Robert The Builder 🇺🇸 的头像

🔨Robert The Builder 🇺🇸1 年前

Makes sense doesn't it.

Yorkshire Lass 的头像

Yorkshire Lass1 年前

Aye, we’re all about the ‘why’—not just the ‘how’—if we’re to trust it in the first place. Proper understanding needs more than a quick explanation; it needs honesty.

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Explainability is a core principle of OpenLedger. It answers two key questions in AI: 1. Who should get rewarded? 2. Why is it failing-bias, drift, or poisoning? If AI can explain why it made a decision & what data influenced it, the entire system becomes more fair, traceable, and resilient.

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