O TYM ODCINKU
A model can be 95 percent accurate and systematically unfair to the people who matter most. The aggregate number hides it.
Treated as a late-stage checklist item, bias gets discovered too late to fix, rationalized away under deployment pressure, or missed entirely. A single accuracy number is not a fairness measure. Measure by subgroup, from the start, where you can still act on what you find.
Long-form whitepaper is at https://themoderncdo.gumroad.com/l/s2e27-bias-as-an-afterthought
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INNE ODCINKI W TYM PODCASCIE
For fifty years, the rule was simple: garbage in, garbage out. Bad data produced output that obviously looked bad, so someone caught it and fixed it. AI broke that bargain. Feed a copilot flawed data and it returns a fluent, confident, well-structured answer that happens to be wrong, and nothing on…
Most AI governance ends at the moment the real risk begins. The day a model goes live is the day it starts to drift.
A model learns a snapshot of the world, and the moment it ships that snapshot starts to age. Accuracy and fairness degrade silently, with no alarm, because the governance was all spe…
Generative AI is most dangerous at the exact moment it is most convincing.
It fails fluently. It produces a confident, polished, authoritative falsehood with no signal that it is wrong, leaks whatever an employee feeds it, and varies its output from one run to the next. Govern the real failure mode…
The moment a real decision can be blamed on a model, you have built a machine for laundering accountability.
A model makes a consequential decision and no human owns it. The data scientist points to the business, the business points to the model, the sponsor treats the output as objective. Everyone…
Zastrzeżenie: Podcast i grafika osadzone na tej stronie pochodzą z Richard Muirhead, który jest własnością jego właściciela i nie jest powiązany ani wspierany przez Listen Notes, Inc.
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