Problem Solving › Data Analysis
Il modello che funziona ma discrimina · Data Analysis · Beginner
The scoring model you built to prioritize loan applications works: it cuts defaults by 20% and has been in production six months. But an analysis you ran out of diligence shows it systematically penalizes applicants from two low-income geographic areas, with double rejection rates at equal actual creditworthiness: the model does not use the address, but reconstructs it through proxy variables. The business will not touch a model that earns, legal says no forbidden variable is formally used, and the AI Act is starting to apply to credit scoring systems. Rebuilding the model without the proxies takes three months and, per first estimates, costs a third of its effectiveness. What do you do?
Il modello di scoring che hai costruito per prioritizzare le richieste di finanziamento funziona: riduce le insolvenze del 20% ed è in produzione da sei mesi. Ma un'analisi che hai condotto per scrupolo mostra che penalizza sistematicamente i richiedenti di due aree geografiche a reddito basso, con tassi di rifiuto doppi a parità di merito creditizio effettivo: il modello non usa l'indirizzo, ma lo ricostruisce da variabili proxy. Il business non vuole toccare un modello che rende, il legale dice che formalmente nessuna variabile vietata è usata, e l'AI Act inizia ad applicarsi ai sistemi di credit scoring. Rifare il modello senza le proxy costa tre mesi e, secondo le prime stime, gli fa perdere un terzo della sua efficacia. Cosa fai?
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