Ethical AI Commitment

Our Commitment to Ethical AI

ThinkReview is committed to using artificial intelligence responsibly and ethically. We believe AI should augment human developers, not replace them, and that AI systems must be transparent, fair, and respectful of user privacy—especially when analyzing source code.

Ethical Principles for Code Review AI

1. Transparency

We are transparent about how we use AI for code reviews:

• Clear Disclosure: We clearly state that code reviews are powered by Google's Gemini AI.
• User Control: You explicitly trigger each code review—AI never analyzes code automatically.
• Review Methodology: We explain how AI reviews work and what types of analysis are performed.
• Model Information: We disclose which AI model is used (Google Gemini) and provide links to its terms and privacy policy.

2. No Code Training

We never use your code to train AI models:

• Analysis Only: Your code is analyzed for review purposes and immediately discarded—never used for training.
• No Model Training: We don't train our own AI models on your source code.
• Third-Party Policies: Code is sent to Google's Gemini API, which has its own policies regarding code usage (see Google's Gemini API Terms).
• Code Ownership: Your code remains your intellectual property—we don't claim any rights to it.

3. Fairness and Bias Mitigation

We work to ensure AI code reviews are fair and unbiased:

• No Discriminatory Analysis: Code reviews focus on technical quality, security, and best practices—not on developer identity or demographics.
• Language Agnostic: AI reviews support multiple programming languages without bias toward specific languages or frameworks.
• Objective Criteria: Reviews are based on objective technical criteria (security, performance, maintainability) rather than subjective preferences.
• Continuous Improvement: We monitor review quality and work to reduce potential biases in AI analysis.

4. Human Oversight

AI assists but doesn't replace human judgment:

• Augmentation, Not Replacement: AI reviews are suggestions to assist developers—final decisions remain with human reviewers.
• Reviewer Discretion: Developers can accept, modify, or ignore AI suggestions based on their expertise.
• Context Awareness: We acknowledge that AI may not understand all business context and encourage human reviewers to apply judgment.
• Continuous Learning: We provide feedback mechanisms to improve AI review quality over time.

5. Privacy and Data Protection

AI processing respects your code privacy:

• Minimal Data Usage: Only the code diff you request is analyzed—no access to full repositories.
• No Code Storage: Code is processed in real-time and immediately discarded—never stored.
• Isolated Processing: Each code review is processed in isolation with no cross-contamination between reviews.
• Secure Transmission: Code is transmitted securely over encrypted connections for AI analysis.

6. Accuracy and Reliability

We strive for accurate and reliable AI code reviews:

• Transparent Limitations: We acknowledge that AI may not catch all issues and encourage comprehensive human review.
• False Positive Awareness: AI suggestions may include false positives—developers should verify all recommendations.
• Continuous Improvement: We work to improve review accuracy through model updates and feedback.
• Clear Indication: Reviews clearly indicate they are AI-generated suggestions, not definitive assessments.

7. Responsible Use

We promote responsible use of AI code reviews:

• Educational Purpose: AI reviews are tools for learning and improvement, not replacements for understanding code.
• Security Focus: AI helps identify security vulnerabilities, but should complement, not replace, security audits.
• Best Practices: Reviews suggest best practices, but teams should adapt suggestions to their specific context.
• No Automated Decisions: We don't support fully automated code approval—human review is always required.

8. Accountability

We take responsibility for our AI systems:

• Error Reporting: We provide mechanisms to report inaccurate or problematic AI reviews.
• Issue Resolution: We investigate and address concerns about AI review quality or ethical issues.
• Transparent Policies: Our AI use policies are publicly documented and regularly reviewed.
• User Feedback: We welcome feedback on how to improve AI review ethics and quality.

How We Use AI for Code Reviews

1. Code Analysis Process

When you request an AI code review:

1. User Initiation: You explicitly click "Review Code" on a merge request or pull request.
2. Code Extraction: Only the code diff (patch) is extracted—not the full repository.
3. Secure Transmission: The diff is securely transmitted to Google's Gemini AI API.
4. AI Analysis: Gemini analyzes the code for security issues, bugs, performance concerns, and best practices.
5. Review Generation: AI generates a structured review with prioritized suggestions.
6. Immediate Discard: The code diff is immediately discarded—never stored.

2. What AI Reviews Analyze

Our AI code reviews focus on:

• Security Vulnerabilities: Potential security issues like SQL injection, XSS, authentication flaws.
• Code Quality: Code organization, readability, maintainability.
• Performance Issues: Potential performance bottlenecks, inefficient algorithms.
• Best Practices: Adherence to language-specific best practices and patterns.
• Bug Detection: Potential bugs, edge cases, error handling issues.

3. What AI Reviews Don't Do

Our AI reviews are limited and don't:

• Make Final Decisions: AI doesn't approve or reject code—humans make those decisions.
• Understand Business Context: AI may not understand business requirements or specific use cases.
• Replace Security Audits: AI reviews complement but don't replace professional security audits.
• Train on Your Code: Your code is never used to train AI models.

Third-Party AI Service

ThinkReview uses Google's Gemini AI for code analysis. We chose Gemini because:

• Privacy Policies: Google has established privacy and data protection policies.
• Transparency: Google provides transparency about their AI models and usage.
• Reliability: Enterprise-grade AI service with high availability and reliability.
• Ethical Standards: Google has commitments to responsible AI development.

However, we acknowledge that using a third-party AI service means your code is processed according to Google's terms and policies. We encourage you to review Google's Gemini API Terms to understand how Google processes code data.

Continuous Improvement

We are committed to continuously improving our ethical AI practices:

• Regular Review: We regularly review and update our ethical AI policies.
• User Feedback: We incorporate user feedback to improve AI review quality and ethics.
• Industry Standards: We follow evolving industry standards for ethical AI use.
• Transparency Updates: We update our policies and disclosures as our AI use evolves.