Automated Karma: Can AI Objectively Rank Discussion Quality?

In the world of online communities, content quality is often measured by simple user interactions like 'likes' and 'dislikes'. But what if we could build a more objective system? A recent discussion delved into the concept of using artificial intelligence to create an automated 'karma' system, designed to score contributions based on their intrinsic quality rather than popularity.

Defining and Measuring Quality with AI

The core idea is to train an AI to assign points to comments based on a set of desirable conversational traits:

• Using correct factual arguments
• Staying on topic and not straying from the original point
• Avoiding misdirection or fallacious reasoning
• Being considerate and reasonable in tone

While the concept is appealing, its practical implementation is a mixed bag of the feasible and the nearly impossible.

The Feasible vs. The Philosophically Hard

According to experts in the field, some of these quality metrics are well within reach of current technology.

• Assessing Civility: Detecting hostility, politeness, or a 'reasonable' tone is a common task for sentiment analysis. Models like BERT, fine-tuned on a labeled dataset of 'hostile' vs. 'not hostile' content, can perform this task effectively and cheaply.
• Maintaining Relevance: Determining if a reply is relevant to its parent comment ('not straying away') is also a solvable problem. One suggested approach is using a Siamese network, a type of neural network that can be trained to determine the semantic similarity or relevance between two pieces of text.

However, the list's very first item—verifying 'correct factual arguments'—is the biggest challenge. This isn't just a difficult engineering problem; it's a deep philosophical one. An AI that could reliably and universally determine 'truth' would be a monumental achievement. Critics of the idea point to concepts like Gödel's incompleteness theorems, which show that even in formal systems like mathematics, there are true statements that cannot be proven. Automating truth verification for the messy, context-dependent world of human language is an exponentially harder problem.

The Human Element and Unintended Consequences

Beyond the technical hurdles, there are fundamental questions about the role of such a system. A key argument against auto-karma is that it fundamentally misunderstands the purpose of 'likes'—they are a way for human readers to express their subjective reaction to a comment. Some discussions are not about objective fact but about opinion, taste, and perspective. An AI moderator would negate this human element.

Taking this idea to its logical conclusion presents a dystopian vision. If the goal is perfect conformity to guidelines, why not have an AI rewrite all comments to be optimally civil and factual? Why not have AI generate entire discussions to achieve maximal information density? This path risks creating sterile, inhuman spaces that lose the very authenticity that makes them valuable.

Are These Systems Being Built in Secret?

A fascinating question raised was why no public-facing platform has rolled out an explicit auto-karma system. Two primary hypotheses emerged:

1. They are a secret, competitive advantage. Like research into room-temperature superconductors, a truly effective automated quality system would be incredibly valuable, and any organization that cracks the code would keep it proprietary.
2. They are kept secret because they fail. The alternate theory is that many have tried, but the systems simply don't work well enough. The problem is too hard, and publicizing a flawed, biased, or easily-gamed system would be a disaster.