Foundational Values for AI Ethics in Framing New Models
Introduction: The rapid advancement of artificial intelligence (AI) has transformed industries, from healthcare to finance, yet it raises profound ethical questions. As AI systems increasingly influence decision-making, embedding ethical principles into their design is critical to prevent harm and foster trust. This article explores the foundational values essential for ethical AI development, emphasizing their integration from inception to deployment. 1. Transparency and Explainability Transparency ensures stakeholders understand how AI models function, while explainability provides insights into decision-making processes. Complex models like deep neural networks often act as "black boxes," obscuring their logic. This opacity can erode trust, especially in high-stakes domains such as criminal justice or healthcare. Techniques like LIME (Local Interpretable Model-agnostic Explanations) and tools such as IBM’s AI Fairness 360 aim to demystify AI decisions. Regulatory framew...