Embed controls into every step of a trustworthy AI model journey
Alerts and monitoring — accurate, transparent, auditable information to share on-demand
Confidence in automation — bring AI model safety and compliance controls up to speed
API-driven integrations — pluggable into any data source and model framework
"As the speed of AI innovation proves challenging for risk and compliance functions, Fairly’s solution addresses this challenge and supports transparency and risk protection, by offering a platform for quality assurance needed for responsible AI.”
UBS, Group Chief Digital & Information Officer and Member of the Group Executive Board, UBS
Proactive AI governance, risk and compliance solution designed and approved by data scientists, model validators, internal auditors, cognitive scientists, ethics and business leaders for financial and ethical risks management.
Data Scientists & Model Developers
Remove guesswork out of AI policies and requirements with automated controls
Reduce time writing reports with intuitive UI/UX design for capturing developmental evidence
Reduce errors and improve model safety using behavioral and cognitive nudge techniques
Model Validators, Risk & Compliance Officers
Reduce turnaround time for validation teams with streamlined workflow
Capture evidence for effective challenge with accountability features
Reduce errors and improve model safety with standardized templates and test suites
Increase auditability and explainability throughout the entire model journey with auditable process-based explainability features
Provide ability for auditing team to audit AI/ML models for bias with our Bias Inspector
Create instant alerts for continuous risk monitoring for real-time risk management
Increase confidence in the AI approval process with customizable executive reports and dashboards
Automate controls to remove discretionary decisions which could introduce bias
Reduce errors and improve model safety to prevent financial, legal, ethical and reputational harms.
Right now AI teams are facing growing regulatory requirements and the impossible task of resourcing a complex new system of model-to-market risk management.
New policies and updates like SR11-7, SS3/18, proposed EU AI regulations, Responsible AI guidelines (300+), industry and company-internal policies, fair-lending act, etc.
Inconsistent and time consuming manual validation and attestation.
Regulatory and compliance reports that differ by authors, readers, committees, countries, regions, jurisdictions, etc.
Iterating towards explanability and monitoring without standardization.
Custom internal tools with administrators that continuously require maintenance
Test for fair machine learning on sensitive data limited on a case-by-case basis.