Advancing Automated Ethical Profiling in SE: a Zero-Shot Evaluation of LLM Reasoning

• URL: http://arxiv.org/abs/2510.00881v1
• Date: Wed, 01 Oct 2025 13:28:26 GMT
• Title: Advancing Automated Ethical Profiling in SE: a Zero-Shot Evaluation of LLM Reasoning
• Authors: Patrizio Migliarini, Mashal Afzal Memon, Marco Autili, Paola Inverardi,
• Abstract summary: Large Language Models (LLMs) are increasingly integrated into software engineering (SE) tools for tasks that extend beyond code synthesis.<n>We present a fully automated framework for assessing ethical reasoning capabilities across 16 LLMs in a zero-shot setting.
• Score: 1.389448546196977
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering (SE) tools for tasks that extend beyond code synthesis, including judgment under uncertainty and reasoning in ethically significant contexts. We present a fully automated framework for assessing ethical reasoning capabilities across 16 LLMs in a zero-shot setting, using 30 real-world ethically charged scenarios. Each model is prompted to identify the most applicable ethical theory to an action, assess its moral acceptability, and explain the reasoning behind their choice. Responses are compared against expert ethicists' choices using inter-model agreement metrics. Our results show that LLMs achieve an average Theory Consistency Rate (TCR) of 73.3% and Binary Agreement Rate (BAR) on moral acceptability of 86.7%, with interpretable divergences concentrated in ethically ambiguous cases. A qualitative analysis of free-text explanations reveals strong conceptual convergence across models despite surface-level lexical diversity. These findings support the potential viability of LLMs as ethical inference engines within SE pipelines, enabling scalable, auditable, and adaptive integration of user-aligned ethical reasoning. Our focus is the Ethical Interpreter component of a broader profiling pipeline: we evaluate whether current LLMs exhibit sufficient interpretive stability and theory-consistent reasoning to support automated profiling.

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