Ethical Analysis of Large Language Models: A Comparison of GPT, DeepSeek, Claude, Gemini, LLaMA, and Qwen

Abstract

An ethical evaluation of Large Language Models (LLM) requires good background knowledge regarding their technical bases; only then is it possible to see how their architectures and training methodologies lead to ethical concerns and biases. The present review evaluates qualitatively the reports produced for GPT, DeepSeek, Claude, Gemini, LLaMA, and Qwen on different aspects of their technical specifications in terms of how design choices shape ethical considerations like bias, fairness, and transparency. We examine how model architecture, data curation strategies, and ethical bias mitigation and propagation is being handled by different large language models. Through our findings, it appears that proprietary models adopt advanced solutions to manage bias but without being transparent. On the other hand, open models are amenable to an independent audit; however, the datasets that are available for public scrutiny usually harbor some biases that are inherent to the datasets themselves. Thus, in our discussions, we emphasize the need for standardized evaluation frameworks to ensure that ethical considerations are parallel to advances in AI in the future.

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