At Coho AI, we are committed to developing and deploying AI solutions responsibly, ethically, and transparently. This document outlines our approach to responsible AI and addresses common customer questions about our AI capabilities, data practices, development methodologies, and compliance with ethical standards.
1. Overview of Our AI Capabilities
Our AI capabilities are designed to deliver real-time actionable insights and drive value for our customers. Key functionalities include:
Behavioral Segmentation: Advanced algorithms segment user behaviors to identify patterns, enabling personalized experiences and strategic decision-making.
Propensity Models: Predictive models that forecast user behaviors, such as the likelihood to purchase, churn, or engage with specific services.
Scalability: Our AI models are designed to handle large-scale data processing with low latency, ensuring real-time responses.
Our models are built to adapt to customer-specific needs and align with industry standards for quality and reliability.
2. Data Used
What Data Do We Use to Train and Fine-Tune AI?
Our models are trained exclusively using product-usage data provided by each customer, ensuring that the AI is uniquely tailored to their needs. This means:
Data Exclusivity: Models are trained only on the product-usage data of the specific customer, and model outputs are exclusively owned and utilized by that customer.
No Cross-Client Data Sharing: Data from one customer is never shared or used to train models for another customer.
How Is Data Handled?
Customer Control: Customers retain full control over the data shared with us. No data is processed without explicit customer consent, and only the data strictly necessary for model training is used.
Retention and Deletion: Data is retained only for the duration of the service or project and securely deleted after the agreed period.
Opt-Out Options
We provide customers with full control over their data. Customers may choose not to share certain datasets, and we offer mechanisms to exclude such data from training processes.
This approach ensures full alignment with our commitment to data privacy, security, and exclusivity.
3. AI Development Methodology
Our AI development follows a rigorous, multi-phase lifecycle to ensure quality, performance, and compliance:
End-to-End Development Process
Data Collection and Preprocessing: Ensuring data is relevant, high-quality, and anonymized where applicable.
Model Development: Iterative model training with frequent evaluations to optimize performance metrics like accuracy, recall, and F1 score.
Deployment: Secure deployment in production environments.
Monitoring: Continuous monitoring to ensure the model’s accuracy and performance in real-world scenarios.
Use of Tools
No Third-Party Models: We do not use third-party AI or pre-trained models in our solutions. All models are developed in-house, ensuring complete control, transparency, and alignment with customer requirements.
Sub-Processors: Data is shared only with our trusted sub-processors as outlined in our Data Processing Agreement (DPA). Each sub-processor adheres to stringent data protection standards, ensuring the security and privacy of all shared data.
Architecture and Data Flow
Our robust architecture integrates secure data pipelines, storage systems, and inference layers.
Detailed architecture diagrams illustrating the end-to-end data flow are available upon request.
Performance Metrics
All models are tested rigorously using standard metrics such as accuracy, precision, recall, and latency.
Example: Our propensity models achieve high precision and recall rates, ensuring actionable and reliable predictions.
4. Responsible AI
We prioritize responsible AI practices to ensure ethical and secure outcomes for all stakeholders.
Privacy
Customer Control: Customers retain full control over the data shared with us. They decide which datasets are provided, and no data is processed without explicit customer consent.
Data Minimization: We use only the data that is strictly necessary for training our models, ensuring that no superfluous information is collected or processed.
Compliance: Our data handling practices comply with GDPR, CCPA, and other applicable regulations to uphold privacy and data security.
Ethics and Bias
In the applicable models, we actively monitor and mitigate bias during model training to ensure fair outcomes.
In the applicable models, regular fairness audits are conducted to identify and correct any unintentional disparities in AI outputs.
Adversarial Protection
Applicable models undergo adversarial testing to identify vulnerabilities and prevent manipulation or misuse.
Safeguards are implemented to ensure the security and integrity of AI outputs.
Transparency
We provide tools and documentation to explain AI decision-making processes in plain terms.
For predictive models, customers can access insights into feature importance and decision pathways, promoting transparency.
5. Compliance
Our AI systems comply with global data privacy regulations, including:
GDPR: Ensuring data protection and privacy for all individuals within the European Union.
CCPA: Protecting the privacy rights of California residents.
Other Regional Laws: We adhere to other relevant data protection laws applicable to our customers’ operations.
We are deeply committed to developing AI responsibly, with a focus on ethics, transparency, and customer-centricity. We welcome any questions or concerns and are always happy to collaborate with our customers to ensure their needs and expectations are met. For any questions, don't hesitate to contact us at [email protected].