Responsible AI
AI Transparency
Understand how our AI works. We're committed to explainability, fairness, and responsible AI practices in every valuation.
Explainability
Fairness
Accountability
Documentation
Responsible AI Principles
Fairness
Ensure equitable outcomes across demographics and product categories
- Demographic parity testing
- Category balance audits
- Geographic fairness checks
- Price range calibration
Transparency
Explain how valuations are generated and what factors influence them
- Feature importance scores
- Confidence intervals
- Data source disclosure
- Methodology documentation
Accountability
Take responsibility for model outputs and provide recourse
- Human review escalation
- Feedback integration
- Audit trails
- Third-party assessments
Safety
Prevent harmful outputs and maintain system reliability
- Outlier detection
- Manipulation resistance
- Graceful degradation
- Incident response
Human Oversight
Review Queue
Low-confidence predictions are flagged for human review.
2.3%
of predictions reviewed
Feedback Loop
User corrections improve model accuracy over time.
15K+
monthly feedback signals
Expert Panel
Domain experts validate edge cases and new categories.
12
certified appraisers
Questions About Our AI?
We're committed to transparency. Reach out to our AI ethics team.
Contact AI Ethics Team