The Half-Measure of Human-Grounded AI Ethics. Within the context outlined thus far, as argued by Coeckelbergh and Sætra, the debate carried on by LLMs ongoing development seems to be between two main point of view: the Marxist concept of “technological determinism” and the “technological instrumentalism” or “technical orthodoxy”.
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Mimicking a Moral Course for LLMs. The first way to stem LLMs from a conversation driving to inappropriate content are the so-called “guardrail messages,” as “I’m sorry, I cannot generate inappropriate or offensive content” or “As a large language model, I cannot … ”.
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A Noisy Digital Environment as a Biased Dataset. As previously analyzed, human language in public and political debates impacts not only how individuals form opinions but also how AI systems, including LLMs, interact with users and further shape political discourse.
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The Human Touch in LLMs.
As previously analyzed, LLMs represent a significant step toward human-like AI interaction. While they lack true understanding, they learn to associate words and interpret meaning through data analysis.
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First Article of Five – The Impact of Polarization and Digital Cacophony on AI-Generated Text – An Introduction
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