We design AI systems with clear accountability, human oversight, and explicit data boundaries—especially in regulated and high-risk environments.
AI is a powerful tool, but it requires careful governance. Our approach prioritizes transparency, control, and predictability over novelty or automation for its own sake.
Human Oversight
We design AI systems that keep humans in the loop:
- Critical decisions require human review and approval
- Clear escalation paths for edge cases and exceptions
- Monitoring and alerting for unexpected behavior
- Regular review of AI system performance and outcomes
Explainability
We favor approaches that can be understood and explained:
- Preference for interpretable models where appropriate
- Clear documentation of model behavior and limitations
- Audit trails for AI-assisted decisions
- Ability to explain outcomes to stakeholders and regulators
Data Boundaries
We maintain strict boundaries around data use:
- Client data is never used to train models without explicit consent
- Clear separation between training data and production data
- Data minimization—collect and use only what is necessary
- Defined retention and deletion policies for AI training data
Risk-Appropriate Design
We match AI capabilities to risk levels. High-stakes decisions in regulated environments receive more conservative designs with greater human oversight, while lower-risk applications may benefit from more automation. We avoid black-box deployments in contexts where explainability and auditability are required.
Continuous Evaluation
- Regular monitoring for model drift and performance degradation
- Periodic review of AI system outcomes for fairness and accuracy
- Mechanisms to identify and address unintended consequences
- Commitment to updating practices as standards evolve
Questions
For questions about our approach to responsible AI, please contact us at hello@yatisphere.com