Bias in AI: A Comparative Analysis of DeepSeek and ChatGPT

Abstract

Language models trained using artificial intelligence (AI) have now become ubiquitous in various fields, such as education, business, healthcare, and entertainment. However, these systems invite ethical questions, not the least of which is how to manage biases and maintain fairness. In this paper, two state-of-the-art AI language models are analyzed and compared: DeepSeek and ChatGPT. It examines how the ethical beliefs and practices of model developers seeking to mitigate bias influence the models' outputs and their real-world implications, using a literature review process. Through examining the strengths and limitations of each model in the context of ethical considerations, this study demonstrates key differences in how responses are generated, informative, and fair. Insights are presented in the context of responsible AI, including recommendations to improve governance and move toward a more equitable AI systems.

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