Use cases

Bias hides in data across every regulated sector.

Here is what Rosa does about it, with numbers.

Financial services

Credit and lending: the Apple Card scenario

Gender removed from the data, gender bias still in the decisions. Rosa finds the proxies and removes the signal, with no fairness/accuracy trade-off. Run it live in the portal.

Read the case Criminal justice

Recidivism: COMPAS, the canonical case

ProPublica showed the harm. Rosa narrows a recidivism model's false-positive-rate gap by race by about 81% (mean, range 54-100%) on ProPublica's two-year cohort while preserving the data's statistics.

Read the case Hiring and HR

Shortlisting and candidate scoring

Published result: standardised age bias in absenteeism predictions cut from 1.81 to 0.13. Rosa finds the proxies without you labelling a single one.

Read the case Healthcare

Clinical risk and resource allocation

The published heart-disease result: equal risk estimates for men and women after Rosa, gender gap effectively eliminated.

Read the case
Public sector and essential services: benefits, eligibility, and access decisions are named high-risk under the EU AI Act and carry the same data-bias mechanics as the cases above. If that is your sector, talk to us and we will walk your scenario through the same method.

Remove the bias from your data. Keep the evidence.

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Free to try. Free for regulators, with no end date. No credit card.