[Verse 1] When algorithms judge who gets the loan Historical data carved in biased stone Representation gaps in training sets Measurement errors place unfair bets Aggregation hides what matters most Evaluation stumbles coast to coast [Chorus] Fair and accountable, transparent too NIST framework guides us through Demographic parity, equalized odds Individual fairness beats the flawed Pre-process, in-process, post-process clean Building ethics into every machine [Verse 2] SHAP values tell us feature weight LIME explains decisions we must rate Attention maps reveal the neural gaze Counterfactuals illuminate the maze Model cards document what systems do Datasheets expose the biased view [Chorus] Fair and accountable, transparent too OECD principles we pursue Demographic parity, equalized odds Individual fairness beats the flawed Pre-process, in-process, post-process clean Building ethics into every machine [Bridge] Fairlearn audits classification schemes AI Fairness Three-Sixty redeems IEEE alignment shows the moral way System cards reveal what models weigh [Verse 3] Before the training, scrub the poisoned well During learning, fairness metrics tell After deployment, monitor the drift Responsible AI is humanity's gift [Chorus] Fair and accountable, transparent too Ethics woven through and through Demographic parity, equalized odds Individual fairness beats the flawed Pre-process, in-process, post-process clean Building ethics into every machine [Outro] Audit, document, then iterate Responsible AI we celebrate
← Unit 5.4 — Cost Optimization & Scaling | Unit 6.2 — AI Regulation & Legal Landscape →