1. Overview
KIAT, Inc. ("KIAT") utilizes artificial intelligence (AI) and machine learning (ML) technologies to provide irrigation recommendations and agricultural insights. This AI Policy explains how we develop, deploy, and govern our AI systems to ensure they are used responsibly, ethically, and transparently.
We are committed to building AI that benefits farmers while maintaining the highest standards of accuracy, fairness, and accountability.
2. Our AI Technology
2.1 What We Build
KIAT's AI systems include:
- Predictive Models: Proprietary machine learning models that forecast soil moisture levels 3-7 days in advance
- Crop-Specific Models: 33 specialized models trained on different crop types and site conditions
- Recommendation Engine: Systems that translate predictions into actionable irrigation schedules
- Anomaly Detection: Algorithms that identify unusual patterns indicating equipment issues or crop stress
2.2 Training Data
Our models are trained on:
- 15-20 years of historical irrigation and soil moisture data
- 2+ million sensor readings from California agricultural operations
- Weather data including temperature, humidity, wind, and evapotranspiration
- Crop growth models and agricultural research data
2.3 Model Performance
Our models are trained on over 2 million sensor readings and validated against historical irrigation decisions. Real-world performance may vary due to environmental factors, sensor calibration, and site-specific conditions.
3. Human Oversight
KIAT is designed as a decision-support tool, not a fully autonomous system. We believe in meaningful human oversight:
- Recommendations, Not Commands: Our AI provides recommendations that farmers review and approve before implementation
- Expert Validation: All models are validated by irrigation consultants with decades of field experience
- Override Capability: Users can always override AI recommendations based on their local knowledge
- Escalation Paths: Unusual situations are flagged for human review
4. Transparency
4.1 Explainability
We strive to make our AI understandable:
- Recommendations include explanations of key factors (e.g., "Irrigation recommended due to forecast high temperatures and declining soil moisture")
- Dashboard displays confidence levels for predictions
- Historical accuracy metrics are available to users
4.2 Limitations
We are transparent about what our AI cannot do:
- Cannot predict equipment failures or infrastructure issues
- Cannot account for factors not measured by sensors (e.g., pest damage, disease)
- Cannot guarantee specific yield or water savings outcomes
- Performance may degrade during extreme weather events or sensor malfunctions
5. Data Use in AI Development
5.1 How We Use Your Data
Customer data may be used to:
- Generate personalized recommendations for your specific fields
- Improve model accuracy for your crops and conditions
- Contribute to aggregated, anonymized datasets for model training
5.2 Anonymization
When data is used for model training beyond your specific account, we remove or obfuscate identifying information including farm names, exact locations, and owner details. Aggregated insights cannot be traced back to individual operations.
5.3 Opt-Out
Enterprise customers may opt out of having their data used for general model improvement. Contact us to discuss data use preferences.
6. Fairness and Bias
We are committed to building AI that works well for all users:
- Diverse Training Data: Our models are trained on data from various farm sizes, crop types, and geographic regions
- Regular Audits: We monitor model performance across different user segments to identify disparities
- Continuous Improvement: When performance gaps are identified, we prioritize collecting additional training data and refining models
7. Security and Reliability
- Model Security: Our models are protected against tampering and adversarial attacks
- Fallback Systems: If AI systems are unavailable, users can access raw sensor data and basic alerts
- Version Control: All model updates are tested and validated before deployment
- Incident Response: We have procedures to quickly address any AI-related issues
8. Accountability
8.1 Responsibility
While our AI provides recommendations, ultimate responsibility for irrigation decisions rests with the farm operator. KIAT is a tool to inform decision-making, not a replacement for human judgment and expertise.
8.2 Liability
As stated in our Terms of Service, KIAT is not liable for crop losses, water costs, or other damages resulting from reliance on AI recommendations. Farmers should use our recommendations alongside their own knowledge and other information sources.
8.3 Feedback
We encourage users to report any issues, inaccuracies, or concerns about our AI systems. User feedback is critical to improving our models. Contact us at support@kiat.ai.
9. Future Development
As AI technology evolves, we are committed to:
- Keeping pace with best practices in responsible AI development
- Incorporating new research on AI safety and alignment
- Engaging with agricultural stakeholders on AI governance
- Updating this policy as our technology and practices evolve
10. Contact Us
For questions about our AI practices or this policy: