AI-Driven Content Scoring: A New Paradigm for Online Community Moderation?

The idea of an AI-driven content scoring system for online communities, where a single AI, guided by publicly documented rules, assigns "votes" to posts and comments, sparks a fascinating debate. This concept aims to address common issues found in traditional human-voted platforms, such as mob mentality, karma farming, and the timing-dependent visibility of content.

The Vision: AI for Coherence and Quality

The core premise is that if an AI, following transparent guidelines, were to reward attributes like originality, clarity, kindness, strong evidence, and creative thinking, while downvoting low effort, repetition, hostility, or bad faith arguments, several benefits could emerge:

• Elimination of Mob Dynamics: Content visibility would depend on meeting stated values rather than pure popularity or timing.
• Coherent Culture: Users would learn to craft content aligning with the AI's principles, fostering a more consistent community culture.
• Skill Development: Posting would evolve into a skill, challenging users to demonstrate their understanding and adherence to community standards.
• Evolving Guidelines: The AI's voting guidance itself could become a subject of community debate, allowing for transparent updates and evolution over time.

This approach is seen by some as a potential antidote to the current "toxic" environment caused by human moderators and the pursuit of superficial upvotes. One participant even shared their experience with a platform called GoodFaith, which uses AI for pre-submission moderation and post-scoring, aligning closely with the proposed system.

Key Concerns and Counterarguments

However, the concept faces several significant challenges and criticisms:

• Semantics and Purpose of "Voting": Critics argue that "voting" inherently implies collective human decision-making, and an AI assigning scores is fundamentally different. Removing human judgment is seen by some as eliminating the core value of community interaction.
• Incentive Shift, Not Elimination: While it removes human-driven karma farming, it could simply replace it with a new form: users optimizing their content specifically to appease the AI's algorithm. The authenticity of expression might still suffer.
• Centralization of Power: A major concern is the increased power concentrated in the hands of those who control the AI's guidance (moderators or platform owners). Transparency of rules, while helpful, might not fundamentally alter this power dynamic, especially with the non-deterministic and often opaque nature of underlying AI models.
• Gaming the System: Public voting rules, while intended for clarity, could ironically make it easier for sophisticated users to "game" the system, potentially leading to a flood of "specifically created spam and slop" that perfectly adheres to the metrics but lacks genuine value.
• User Frustration and Blame: If the AI misinterprets content or assigns scores incorrectly (e.g., perceiving a valid rebuttal as unkind), users could experience significant frustration and direct their blame towards the platform and its system, rather than individual human voters.
• Authenticity of Interaction: Many desire to write for other real humans, not for an artificial intelligence, questioning the fundamental nature of community if the primary arbiter of value is a machine.

Alternative AI Applications

Instead of an AI acting as a post-publication judge, an alternative application was suggested: using AI as a real-time assistant during content creation. This could involve an AI evaluating a user's draft post or comment and offering suggestions for improvement (e.g., pointing out snark, personal attacks, or lack of evidence) before submission. This approach empowers the user to make changes and retain agency over their final output, addressing some of the concerns about frustration and control.

Ultimately, the discussion highlights the complex interplay between technology, community dynamics, and human behavior in online spaces. While AI offers promising solutions for content quality and moderation, careful consideration must be given to power structures, potential for manipulation, and the fundamental nature of human interaction it aims to mediate.