[Verse 1] When we build machines that learn and decide We must guide them with principles as our guide Fairness first, let every voice be heard Accountability means we stand by our word Transparency shows how decisions are made Don't let bias lead our systems astray [Chorus] F-A-T, Fairness Accountability Transparency Build it right from the start, AI with heart F-A-T, check your bias systematically Pre-process, in-process, post-process with care Responsible AI is everywhere [Verse 2] Demographic parity means equal rates across groups Equalized odds when predictions loop Individual fairness treats similar cases the same Historical bias carries forward the pain Representation gaps in data we collect Measurement errors we must detect [Chorus] F-A-T, Fairness Accountability Transparency Build it right from the start, AI with heart F-A-T, check your bias systematically Pre-process, in-process, post-process with care Responsible AI is everywhere [Bridge] SHAP and LIME help explain the why Attention maps show where models focus their eye Model cards document what we've built Datasheets reveal where bias might wilt NIST framework guides our way OECD principles light the day [Verse 3] Aggregation bias when we combine too wide Evaluation metrics can be our guide Fairlearn and AI Fairness three-six-oh Help us audit what our models show IEEE ethics keep us aligned Responsible AI for all mankind [Chorus] F-A-T, Fairness Accountability Transparency Build it right from the start, AI with heart F-A-T, check your bias systematically Pre-process, in-process, post-process with care Responsible AI is everywhere [Outro] Document, audit, explain each choice Give every community their voice F-A-T principles lead the way For responsible AI every day
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