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ai

Last modified by Ryan C on 2025/03/06 23:28

Overview

Artificial Intelligence (AI) refers to the simulation of human intelligence by machines, particularly computer systems. AI encompasses various disciplines, including machine learning, natural language processing, and neural networks, and has grown to influence countless aspects of modern life, from healthcare to entertainment.

While AI has the potential to revolutionize industries, it is not without controversy. Concerns about bias in AI systems, ethical implications of training practices, and notable incidents like the Gemini AI Incident highlight the challenges of integrating AI into society responsibly.

How AI is Trained

AI models are trained using vast datasets, which can include text, images, videos, and other forms of structured and unstructured data. The process typically involves:

  1. Data Collection: Amassing extensive datasets to teach the AI patterns, associations, and concepts.
  2. Training: Employing algorithms to process the data and adjust internal weights, enabling the model to perform specific tasks.
  3. Validation: Testing the AI on separate data to ensure it can generalize beyond the training dataset.
  4. Deployment: Implementing the trained model into real-world applications, where it can operate and evolve further based on user interactions.

Many of the hate detection models are trained on toxigen, who by their own admission seek to ultimately shift power dynamics towards nonwhites.

toxigen

Bias in AI Systems

AI bias arises when training data or algorithms disproportionately favor certain perspectives, demographics, or outcomes, leading to inequitable or erroneous results. Sources of bias include:

  • Dataset Imbalance: Overrepresentation or underrepresentation of certain groups within training datasets.
  • Algorithm Design: Models inherently reflect the priorities and assumptions embedded in their code.
  • User Feedback Loops: AI systems may perpetuate biases when retrained on user-generated data, which can reinforce existing prejudices.

We can see the specifics of how these biases are coded into modern AI hate detection models, which are used across most social media platforms to determine when something is "hateful". This is how you end up being shadowbanned or suppressed on social media.

AI Censorship of "hateful" content

  1. Flagging Toxic Tweets:
    • AI detects tweets containing hate speech, explicit toxicity, or harassment and flags them for review.
  2. Content Warning Labels:
    • Models are used to place labels like "This Tweet may contain sensitive content" on posts with potentially offensive material.
  3. Account Suspension and Shadow Banning:
    • Accounts identified as repeatedly posting hateful or toxic content are suspended or shadow-banned (reduced visibility).
  4. Ad Moderation:
    • Twitter uses AI to prevent hateful content in promoted tweets or advertisements.
  5. Hashtag and Trend Monitoring:
    • AI models monitor trending hashtags for coordinated hate speech campaigns or implicit toxicity.

Suggestions for Further Reading