AI Transparency: Explaining the Black Box's 'Why'
AI transparency means seeing the 'why' behind an algorithm's decision, not just its code. It's vital for high-stakes systems like credit scoring or news feeds. The footgun is thinking open-sourcing the model is enough; true transparency explains the logic.
AI transparency is the principle that factors influencing an algorithm's decision should be visible and understandable. Think of it like a receipt for a decision, showing the 'items' that led to the final 'total'. This is essential for systems with real-world impact, from credit scoring and hiring tools to the news you see online, allowing for checks on fairness and bias. The main footgun is confusing code visibility with decision transparency; seeing the code doesn't explain why *your* specific loan was denied.
Read the original → Wikipedia: Algorithmic transparency
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