Ethical Behavior Discrimination Based on Social News Dataset

With the broader applications of artificial intelligence (AI), their ethical and moral issues have attracted more and more concerns. How to develop an AI system that complies with human values and ethical norms from the perspective of technology realization, namely, ethical aligned AI design, is one of the important issues that need to be solved urgently. The ethical and moral discrimination based on machine learning is a beneficial exploration in this aspect. Social news data has rich content and knowledge of ethics and morality, which provides the possibility for the training data development of machine learning. Because of this, this paper constructs a social news dataset with ethics and morality of human behavior, which is attached to law and code of conduct dataset for machine learning training and testing. The ethical behavior discrimination model ERNIE-CNN based on enhanced language representation of information entities (ERNIE) and convolutional neural network (CNN), is developed to extract ethical discriminations about behavior by calculating semantic similarity based on the vector representation of words. The experimental results show that the proposed model has better performance than the baseline models.