[Verse 1] When machines make decisions for you and me They might not treat everyone equally Hidden patterns in the training data Can create outcomes that just ain't fair A loan denied, a job passed by Resume screening with a biased eye We need to check what's going wrong Before these systems lead us on [Chorus] Detect, test, monitor every day Keep algorithmic bias at bay Check your data, test your code Watch for patterns down the road Detect, test, monitor the flow Make sure fairness starts to show Build it right from the start Give every user a fair part [Verse 2] Start by looking at your dataset close Are all groups represented the most? Historical bias baked right in Will make your algorithm spin Gender, race, and age divides Can create discriminating sides Clean your data, balance the scale Before your fairness starts to fail [Chorus] Detect, test, monitor every day Keep algorithmic bias at bay Check your data, test your code Watch for patterns down the road Detect, test, monitor the flow Make sure fairness starts to show Build it right from the start Give every user a fair part [Bridge] Statistical parity, equal odds Demographic parity beats the flaws A-B testing splits the groups Confusion matrix shows the loops False positive rates across the board Should be equal, that's the word Fairness metrics guide the way To build systems that won't betray [Verse 3] In production, never stop the watch Monitoring systems, never botch Dashboard metrics, alerts that ring When bias starts to do its thing Regular audits, human review Keep the algorithms fair and true Continuous learning, feedback loops Help your system avoid the scoops [Chorus] Detect, test, monitor every day Keep algorithmic bias at bay Check your data, test your code Watch for patterns down the road Detect, test, monitor the flow Make sure fairness starts to show Build it right from the start Give every user a fair part [Outro] Technology should lift us all Not make some people hit the wall With careful testing, watching close We can build the systems most Fair and just for everyone Algorithmic bias, we have won
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