The article explores how AI, particularly large language models (LLMs), exposes biases that inform our judgments of intelligence, often linked to race, gender, and social status. Traditionally, human contributions to intellectual discourse are judged by physical presentations, yet LLMs lack such identities. This situation raises critical questions about whether intelligence is genuinely evaluated on merit or influenced by societal narratives. While AI can help democratize knowledge by divorcing ideas from their origins, it also perpetuates existing biases due to its training on flawed data, leading to a complex dialogue about meritocracy in thought.
For centuries, we've filtered thought through gender, race, and status, crowning those who fit the cultural, political, or social script.
We mistake physical presentation for intellectual merit when judging who deserves to be heard.
When insight comes without a human mask, it can be almost raw and disarming.
This isn't about AI being neutral. Trained on our messy world, these systems inherit our flaws.
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