Sustainable Development Goals

Abstract/Objectives

This course aims to guide students in understanding and critically reflecting on the ethical, risk-related, and social issues involved in artificial intelligence (AI) technologies and their applications. First, the course introduces the historical development of machine learning–based AI, relevant international ethical guidelines, and basic concepts in philosophical ethics. Second, it explains how the sources and classification of data influence AI predictions and decision-making. Next, the course examines the design of AI models and algorithms, analyzing the social and political issues associated with AI technologies. In addition, the course discusses the ethical and social controversies arising from AI applications, including existential concerns about human survival, bias and discrimination, the intensification of existing social inequalities, gender stereotypes, and issues related to labor supply chains and exploitation. Finally, the course expects students to propose feasible solutions to existing AI projects that have generated ethical and social controversies.

Results/Contributions

After completing this course, students will develop a solid foundation in AI ethics and gain clear criteria for evaluating ethical issues in AI systems. They will understand major international ethical guidelines as well as key concepts from moral philosophy, and apply them to analyze risks across the AI lifecycle—from data collection, labeling and classification, to model design, deployment, and governance. Students will be able to identify and explain how algorithmic bias, discrimination, and automation-driven inequality emerge, and assess their impacts on gender, human rights, labor conditions, and AI supply chains. Drawing on real-world cases, they will propose feasible mitigation strategies such as stronger data governance, transparency measures, accountability mechanisms, risk assessment practices, and stakeholder communication. Through reading notes, worksheets, and structured class discussions, students will strengthen critical thinking and ethical reasoning skills. In the final group project, they will demonstrate collaborative problem-solving by completing a case-based analysis with effective written and oral presentation, while also practicing cross-disciplinary (and potentially cross-institutional) teamwork and resource sharing—supporting quality education (SDG 4) and global partnerships (SDG 17).

Keywords

Ethics of Artificial IntelligenceMachine LearningAlgorithmsSocial InequalityAI Governance and Ethical Guidelines

Contact Information

劉秋宜
philos@my.nthu.edu.tw