Responsible AI Implementation Checklist

Data Privacy & Governance

Data Minimization: We only collect user data that is strictly necessary for our core functionality.

User Consent: We obtain clear, explicit user consent before collecting or processing any personal data.

Privacy Policy: Our privacy policy is easily accessible and clearly explains how we use AI and handle data.

Data Deletion: Users have a straightforward way to request the deletion of their data.

Data Auditing: We have audited our training data (if any) for potential sources of representation bias.

Output Testing: We regularly test our AI's outputs across different demographic segments to check for fairness.

Bias Mitigation Strategy: We have a documented strategy for identifying and mitigating detected biases.

Inclusive Prompts: Our prompt engineering process includes checks for inclusive language and avoids stereotypes.

AI Disclosure: We clearly inform users when they are interacting with an AI system versus a human.

Outcome Explanation: For significant AI-driven decisions (e.g., loan denial), we have a process to provide a simplified explanation.

Model Limitations: We have documented and can communicate the known limitations and appropriate uses of our AI.

Terms of Service: Our ToS clearly outlines the role of AI in our service delivery.

Prompt Injection Defense: We sanitize user inputs to protect against malicious prompt manipulation.

Data Leakage Prevention: We have protocols to prevent the AI from exposing sensitive training or user data.

Adversarial Testing: We periodically test our system against inputs designed to make it fail or behave unexpectedly.

Secure Data Handling: All data, both in transit and at rest, is encrypted using industry-standard protocols.

Human-in-the-Loop: For high-stakes decisions, a qualified human reviews and approves the AI's output.

Appeal Process: Users have a clear way to appeal or request a review of a significant decision made by our AI.

Ownership: There is a clearly defined person or team responsible for our AI system's performance and outcomes.

Incident Response Plan: We have a plan in place for if our AI system causes harm or makes a serious error.