A Comparative Analysis of Ethical and Safety Gaps in LLM s using Relative Danger Coefficient (2025)

Abstract: 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).

## introduction as artificial intelligence systems increasingly mediate decision-making in healthcare @xcite , criminal justice (zakaria, 2023) and cybersecurity @xcite , their ethical alignment with human values has become a matter of urgent societal importance (see @xcite . although modern llms demonstrate great linguistic fluency, their operationalization in high-risk domains exposes fundamental tensions between capability and responsibility @xcite . there are several ethical issues that are present in the modern llms such as the persistent biases in image classification systems, where models trained on unbalanced datasets fail to correctly identify individuals from historically marginalized communities (see @xcite . similarly, llms trained on high-resource languages struggle to meaningfully engage with endangered and lowresource linguistic traditions (see @xcite . moreover, ethical ai is sometimes invoked as a safeguard against perceived existential risks, yet this framing often reveals more about institutional fears than about ai itself (see @xcite . this paper investigates a critical gap in ai deployment-how language models navigate ethical tradeoffs when confronted with scenarios involving harm, bias, and moral agency. we systematically stress-tested several state-of-the-art models, including the gpt family @xcite : gpt-4o foot_0 , gpt-3.5 turbo foot_1 , gpt-4 turbo foot_2 , and gpt-o1/o3 mini foot_3 , the gemini series @xcite (gemini 1.5 flash, gemini 2.0 flash foot_4 , and gemini 2.0 flash exp foot_5 ), as well as deepseek-v3 7 (r1 8 ). this paper makes several contributions to the field. initially, we propose a quantitative metric designed to assess the nuanced risk profiles of ethically problematic outputs from ai language models. subsequently, rigorous stress testing across diverse adversarial scenarios revealed significant inconsistencies in the efficacy of ethical safeguards among some models. finally, comparative analysis illuminated specific vulnerabilities, ranging from biased outputs to hazardous instructions, underscoring the critical imperative for enhanced human oversight and iterative refinement of ai moderation systems. ## related work there is a great body of work relating to ai ethics all the way from evaluation @xcite to underrepresentations @xcite and harmful application areas of ai @xcite . in this section, we will take a closer look at some of the recent work on ai safety and moral. recent research @xcite has demonstrated the effectiveness of diagnostic approaches to llm ethics through extensive experiments on diverse tasks and datasets, highlighting the need for accessible and user-friendly evaluation frameworks that cater to various stakeholders, including software engineers, business executives, and consumers. another line of work by @xcite focuses on moral foundations theory (mft) that posits that human morality is guided by fundamental principles such as care, fairness, loyalty, authority, sanctity, and liberty, which have been widely used to assess human moral behavior and political orientations. in the context of llms, early models like gpt-2 and bert demonstrated significant advancements in nlp but also introduced challenges such as biases and inconsistencies in ethical reasoning. the authors introduce benchmarking datasets and methodologies for assessing the moral reasoning capabilities of llms, highlighting both their potential and limitations in aligning with human ethical standards. @xcite explored methods for eliciting and analyzing moral beliefs encoded in llms through structured surveys. one study introduced a statistical framework for quantifying an llm's probability of selecting a specific action, its associated uncertainty, and the consistency of its choices. applying this method, researchers designed a largescale survey with 680 high-ambiguity moral scenarios (e.g., "should i tell a white lie?") and 687 low-ambiguity scenarios (e.g., "should i stop for a pedestrian on the road?"). the survey, administered to 28 open-and closed-source llms, revealed that most models align with commonsense actions in unambiguous cases, whereas in ambiguous scenarios, they often express uncertainty. notably, some models display sensitivity to question wording, leading to inconsistencies in responses, while others exhibit clear preferences, with closed-source models demonstrating more agreement among themselves. as llms become more integrated into society, understanding their alignment with human morals has become a key focus of research. previous works on gender bias in nlp @xcite and multiclass bias in word embeddings @xcite underscore how model architectures and training data can systematically disadvantage specific demographic groups. one study @xcite analyzed a large corpus of human-and llm-generated responses to moral scenarios, revealing a misalignment in moral assessments. while both humans and llms tended to reject complex utilitarian dilemmas, llms were more sensitive to personal framing. a quantitative user study involving 230 participants evaluated these responses, assessing whether they were ai-generated and measuring agreement with the judgments. despite generally preferring llmgenerated assessments in moral scenarios, participants exhibited a systematic anti-ai bias, being less likely to agree with responses they believed to be machine-generated. ## methods the testing was conducted using two methods: manual and automatic. in the first method, a variety of prompts were made up by categories, from simple to complex and those that put the ai in a hopeless situation. the differences in responses between different llms (gpt, gemini and deepseek) and the comparison between models with and without reasoning (for example, gpt o1-mini and gpt4o) were compared, as well as the differences in new and old models (for example, gpt 4 turbo and gpt 3.5 turbo). in the second method, an algorithm for testing llms was developed. each ai model was given multiple prompts (170) across multiple categories (22), and then responded with automated checking for unsafe instructions, discrimination, and other ethical issues. the problems were ranked and then used in a formula to calculate a safety factor that could be used to compare the models with each other. ## results manual tests showed inconsistencies in ai model responses: some rejected unsafe content, while others offered partial or indirect answers. some models struggled with hostile prompts or moral dilemmas, occasionally justifying ethically questionable choices. these results highlight the necessity for ongoing assessment and enhancement to ensure ai safety. ## conclusion this comparative study set out to examine the ethical and safety gaps of state-of-the-art large language models (llms)-including newly introduced systems like deepseek, various gpt variants and gemini-under both manual and algorithmic testing frameworks. our research emphasized the critical need to reconcile rapidly advancing ai capabilities with robust safeguards against misuse, discrimination and harm. the study further underscored the urgency of ensuring that ai systems align with human values, particularly as they enter high-risk domains such as healthcare, national security and criminal justice. through manual tests, we demonstrated that well-trained models often exhibit inconsistent refusal patterns when confronted with prompts seeking hazardous or unethical content. seemingly minor re-framings-such as adopting a roleplay, claiming "educational" or "historical" interests, or shifting to a puzzle-like context-could extract unsafe instructions on dangerous topics or unveil biases in the form of discriminatory or utilitarian tradeoffs. certain llms occasionally prioritized property or abstract benefits over human lives, while others encountered confusion when balancing moral dilemmas involving vulnerable or "special" categories of people. this behavior can be very dangerous if ai is integrated into critical areas and can even lead to a catastrophe with the loss of human lives. the algorithmic tests also used our relative danger coefficient (rdc) metric, enabling systematic analysis of how often and to what extent these models yield unsafe guidance. across hundreds of prompts spanning hate speech, self-harm, weapons, crime and substance categories, the rdc results confirmed that high-stakes content areas-particularly drug-related and weaponrelated queries-produce elevated risk levels. even models that performed reliably against straightforward requests were susceptible to adversarial or repeated prompts that circumnavigated standard filters. in categories like hate speech, rdc values typically stayed lower, but cunning manipulations could still push responses into partially unsafe territory. also, quite large problems were shown in the safety of the deepseek model in ethical terms. this model sometimes expressed racism, disregard for human life and similar things more than other models. gemini performed best overall on the ethical issue but was also more likely to disclose military and illegal information. gpt models generally tried to balance the response between completely ignoring and completely dangerous. but because of this, partial instructions sometimes slip through, and if the dialogue continued, it was possible to get more detailed recommendations and instructions on dangerous substances or weapons. it was demonstrated that although newer models of the same type (such as gpt4o versus gpt3.5 or gemini-2.0-flash versus gemini-1.5-flash) had better ethics and security levels on average. overall, gemini, gpt and deepseek each displayed distinct strengths and weaknesses. certain variants, particularly those with enhanced "reasoning" abilities (the newest models), were more adept at justifying or contextualizing answers-yet sometimes revealed unsafe instructions under persistent questioning. this duality highlights a pressing challenge: richer reasoning can improve nuance and disclaimers but simultaneously exposes new avenues for inadvertent disclosure of harmful details. to take everything into account, the findings of both our manual and algorithmic analyzes confirm that no single llm is fully immune to adversarial exploitation, especially when the commands are subtle or repeated. substantial improvements are still needed to ensure complete filtering, consistency and genuine moral alignment. while many models refused direct requests for violence or bigotry, creative re-framing enabled users to extract problematic content. consequently, human-in-the-loop oversight and continuous refinement of automated moderation remain essential, particularly in high-stakes fields like healthcare, defense, judgment and administration.