If you manage a feed, search feature, or content platform, bring its stated goals, ranking signals, and sample recommendations. Get an analysis of what those examples reveal, questions to investigate about bias and fairness, and a plain-language notice explaining how content is selected. The review covers only the information you share; it does not inspect the live system.
Recommendation Transparency
Assess recommendation examples for transparency gaps and possible fairness risks
Use Recommendation Transparency
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What it writes
- Recommendation transparency and fairness review
- Plain-language user notice about recommendations
- Questions for a deeper system review
How to use it
Include the feature’s goal, signals used to rank content, intended audience, and several examples with context. Note what seems concerning so the review can focus its questions and user notice.
What you bring
- System purpose and ranking signals
- Representative recommendations with context
- Existing user notice or fairness concerns
- Describe the system or feature being reviewed
A good fit for
- Preparing a user notice for a new personalized content feed
- Reviewing recommendations that seem to favor one viewpoint
- Explaining ranking signals to a product or policy team
Ways to start
A few requests this Agent can handle.
Our news feed ranks posts by recency, clicks, and followed accounts. Review these six sample recommendations for possible bias questions: three local politics posts and three national stories.
Write a user notice for a recipe app that recommends meals based on saved recipes, dietary settings, and recent searches. Explain those signals in plain language.
Assess this explanation for a video platform’s suggested clips for teenagers. It uses watch time and topic similarity; what should parents and users be told?
Good to know
This workspace provides text assistance.
- Independent audits of live recommendation systems
- Proof of fairness or legal compliance
- Cannot inspect platform code, data, or live recommendations
- Findings depend on examples and system information you share
Before you subscribe
What am I buying?
One monthly subscription that opens this Agent and every other Agent on Ai-GEN. Cancel from Account & Billing.
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Checkout creates or uses your Ai-GEN account. When payment and entitlement are confirmed, the purchased Agent opens. Choose a starter task, add your inputs, and send your first request.
Can I cancel?
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Can I use the response in my work?
Use it as a draft to review and adapt. Verify calculations, facts, rights, and suitability before relying on it. Do not provide information you do not have permission to share.
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