Ethical AI Assessment: A Framework with Composite Scores

The approach we employ initially involves the identification of a wide array of ethical indicators, which include but are not limited to fairness, openness, accountability, privacy, and bias mitigation. Next, we present a methodical approach for establishing uniformity and measuring these indicators. Through the allocation of suitable weights and significance values to each metric, we generate composite scores that provide a lucid and succinct depiction of an AI system's ethical status. We illustrate the pragmatic feasibility of our method by conducting case studies that involve AI systems implemented in real-world scenarios. These case studies demonstrate how the unified metric system may not only pinpoint ethical issues but also enable comparisons across various AI models or iterations. Our research provides a systematic and cohesive approach to evaluating the ethical aspects of AI systems, thereby contributing to the current discussion on AI ethics. Through the consolidation of measurements into composite scores, we improve the clarity and understandability of AI ethics judgments.