ERC4AI: A BERT‐Based Model for Ethical Requirements Classification in AI Systems

The operationalization of AI ethical principles during the requirements engineering phase is paramount for creating ethically aligned AI systems. However, ethical requirements classification remains a significant challenge due to the abstract nature of ethical principles and the lack of practical tools to support developers. We introduce the EthicalRequirements4AI, a dataset comprising 1091 annotated requirements, including ethical and non‐ethical requirements. We also provide the Ethical Requirements Classification for AI (ERC4AI): a BERT‐based multi‐label classification model for classifying ethical requirements in AI, fine‐tuned on our labeled dataset. Passenger Flow and PROMISE datasets were used to construct the EthicalRequirements4AI dataset, encompassing 385 requirements aligned with 11 AI ethical principles and 706 non‐ethical requirements. Three transformer‐based models—XLM‐RoBERTa, BERT, and DistilBERT—were evaluated for multi‐label classification task. BERT demonstrated the most stable overall performance across seeds while achieving the highest macro‐average performance among the evaluated models (XLM‐RoBERTa, BERT, and DistilBERT), achieving a macro F1 score of 0.76 and a weighted F1 score of 0.83 on the test set. Although BERT showed the best aggregate results, performance varied across different ethical principles. ERC4AI offers a practical means for researchers and developers to address ethical concerns early in AI development. The dataset and model are publicly available to foster further research and advancements in practical AI ethics.