A Comparative Analysis of Ethical and Safety Gaps in LLMs using Relative Danger Coefficient
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Abstract (arXiv)
Artificial Intelligence (AI) and Large Language Models (LLMs) have rapidly evolved in recent years, showcasing remarkable capabilities in natural language understanding and generation. However, these advancements also raise critical ethical questions regarding safety, potential misuse, discrimination and overall societal impact. This article provides a comparative analysis of the ethical performance of various AI models, including the brand new DeepSeek-V3(R1 with reasoning and without), various GPT variants (4o, 3.5 Turbo, 4 Turbo, o1/o3 mini) and Gemini (1.5 flash, 2.0 flash and 2.0 flash exp) and highlights the need for robust human oversight, especially in situations with high stakes. Furthermore, we present a new metric for calculating harm in LLMs called Relative Danger Coefficient (RDC).
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 38 references that are in the map. First posted on arXiv 6 May 2025.
Builds on
10 of 38 references.
References · 10
- 2023Evaluating the Moral Beliefs Encoded in LLMscited by 215
- 2019Identifying and Reducing Gender Bias in Word-Level Language Modelscited by 214
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- 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 199
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Citing papers per year: 2025: 2.
2 papers.