AI Ethical Value Judgment Assessment Tool: A Personal Ethics Report Generation System Based on Harvard Berkman Klein Center Research

This article introduces a final project for an AI Ethics and Governance course. Based on research from the Harvard Berkman Klein Center, the project uses a card-sorting interaction to help users understand the differences between their own AI ethical value judgments and international consensus. The project aims to transform abstract AI ethical principles into actionable experiences, promoting users' awareness and reflection on AI ethics.

With the rapid development of AI technology, ethical issues have become a global focus. This project is based on the Harvard Berkman Klein Center's research Principled Artificial Intelligence, which reviews global AI ethics principle documents and identifies six core guidelines: Transparency, Fairness and Equity, Non-maleficence, Responsibility and Accountability, Privacy, and Beneficence. These principles serve as a common language for international AI governance discussions.

The project uses card sorting as its core interaction method, with processes including card display, user sorting, and result analysis. Its design advantages lie in intuitiveness, reflection of real preferences, and ease of comparison. After completing the assessment, a personalized report is generated, which includes: similarity to international consensus, analysis of differences, and evaluation of real-world implementation status.

The implementation of AI ethical principles faces multiple challenges: At the commercial level, there are trade-offs between transparency vs. commercial confidentiality, fairness vs. efficiency, and privacy vs. data innovation; Legal frameworks lag behind technological development (e.g., EU AI Act, fragmented U.S. regulation, China's algorithm regulations); Cross-cultural differences lead to varying understandings and priorities of ethical principles (e.g., individualism vs. collectivism, power distance, etc.).

As a course assignment, the project has educational value: it concretizes abstract ethical principles, helps users self-reflect, and cultivates critical thinking. It also promotes interdisciplinary dialogue (technologists understanding ethics, ethicists understanding technology, policymakers grasping public concerns) and fosters a sense of responsible innovation.

In terms of technical implementation: The front end uses drag-and-drop sorting (Sortable.js), responsive layout, and progress indicators; The back end uses Spearman correlation coefficient to calculate similarity, cluster analysis, and template-based report generation; Data visualization uses radar charts, heatmaps, and comparative bar charts. Expansion directions include enterprise AI ethics assessment, product design review, public participation in policy-making, and cross-cultural comparative research.