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 justiceRecidivism: COMPAS, the canonical case
ProPublica showed the harm. Rosa reduces a recidivism model's measured race-disparity by ~75-90% (a 6× reduction) while preserving the data's statistics.
Read the case Hiring and HRShortlisting 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 HealthcareClinical 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.
Free to try. Free for regulators, with no end date. No credit card.