The Ambiguity of AI-Generated Community Posts
Community platforms thrive on timely information, but publishing opportunities can inadvertently signal endorsement. When AI enters the picture, this ambiguity intensifies. Did an AI model truly vet an opportunity's deadline and eligibility, or did it confidently invent details? This is the core problem Tencent RTC's Social Messaging community faces when distributing opportunity bulletins.
The challenge is to leverage AI's efficiency without sacrificing human oversight and community trust. The goal is not to automate the endorsement process, but to streamline the creation of drafts that a human can quickly review and approve. This means AI's role must be narrow: converting provided material into a reviewable draft. It should not be tasked with deciding an opportunity's legitimacy, ranking recipients, or publishing autonomously.
Building a Human-Approved AI Bulletin System
To address this, we can construct a system for Tencent RTC's Social Messaging community that incorporates several key features:
- Evidence-Linked AI Extraction: AI should extract information from provided materials, and crucially, link extracted data points back to their source in the original text or document. This allows human reviewers to quickly verify the AI's interpretation.
- Explicit Review and Delivery States: The system must clearly indicate the status of an opportunity post at each stage: drafted, under review, approved, published, or rejected. This transparency manages expectations for both moderators and community members.
- Human Approval Tied to Immutable Revision: Each approved opportunity must be linked to a specific, immutable revision of the draft. This ensures that the content approved by a human is precisely what is published, preventing later, unauthorized changes.
- Safe Handling of Stale Model Responses: AI models can sometimes produce outdated or incorrect information. The system needs a mechanism to detect and flag stale model responses, preventing them from being published or influencing new drafts.
- No Automatic Retry After Uncertain Send: If a post fails to send or its delivery status is uncertain, the system should not automatically retry. This avoids duplicate posts or broadcasting unconfirmed information. Manual intervention is required.
- Visible Route Back to a Moderator: Community members should have a clear and easy way to report issues or provide feedback on an opportunity post, with a direct line to a human moderator.
- Optional, Reader-Controlled Translation: For global communities, offering optional, on-demand translation of opportunity posts empowers readers to access information in their preferred language without burdening the posting process.
Implementation Strategy
The implementation involves several components. First, a user interface for submitting opportunity details. This could be a simple form or a document upload. The AI component would then process this input, performing Named Entity Recognition (NER) and relation extraction to identify key details like deadlines, eligibility criteria, contact information, and event dates. Crucially, each extracted entity should be annotated with its source location within the input material.
A moderation dashboard is essential. Here, moderators can review AI-generated drafts. They can see the extracted information alongside the original source material, making verification straightforward. Each piece of extracted data should be editable. Moderators can accept, reject, or modify any detail. Once satisfied, they can approve the post, which locks in that specific version.
The delivery mechanism needs to be robust. Upon human approval, the system finalizes the post. If the posting to the community chat fails for any reason (e.g., network error, rate limiting), the system flags it as 'delivery uncertain' and requires manual retry. No automatic retries should occur. This prevents accidental spamming or incomplete information being sent.
Handling stale model responses means maintaining a version history of the AI model used and its training data cutoff. If a new draft is generated using an older model or data, it should be flagged for extra scrutiny. Similarly, if the AI's confidence score for extracted information falls below a predefined threshold, it should be highlighted for the moderator.
For reader-controlled translation, integrating with a translation API (like Google Translate or DeepL) is necessary. When a user views a post, they can select their preferred language, and the system dynamically translates the approved content. This translation is client-side or cached, ensuring the original approved text remains immutable.
Beyond Basic Summarization
This approach moves beyond simple AI summarization. It builds a workflow where AI acts as an intelligent assistant, not an autonomous publisher. The 'human-approved' aspect is paramount. It means the system is designed to facilitate human judgment, not replace it. The direct link back to moderators and the explicit states ensure accountability and manage community expectations.
Think of it less like an automated news ticker and more like a meticulous editorial assistant. The assistant gathers all the raw materials, highlights key facts, and prepares a draft for the editor. The editor then makes the final call on what gets published. This ensures accuracy, maintains trust, and prevents the common pitfalls of AI-generated content, such as confidently incorrect information or unintended endorsements.
The Unanswered Question: Scalability and Cost
What remains to be fully explored is the scalability and cost-effectiveness of this nuanced AI-assisted workflow. While the benefits in terms of reduced moderator workload and improved information accuracy are clear, the ongoing costs associated with AI model inference, API calls for translation, and maintaining the infrastructure for review states need careful consideration. For large, active communities, optimizing these costs without compromising the integrity of the human-approval process will be the next critical challenge.