Paper Type

Complete

Abstract

The rapid growth of generative artificial intelligence (GenAI) means that we need strong ethical guidelines and governance frameworks to build trust, stop harm, and make sure that new ideas fit with the values of society. This paper summarizes the underlying theories and established international frameworks that inform the development of dependable generative AI. It analyses emerging trends, underscores critical issues, and evaluates mechanisms for implementing ethical principles in generative AI systems by reviewing governance initiatives, including the NIST AI Risk Management Framework, the European Union's Ethics Guidelines for Trustworthy AI, and corporate commitments such as IBM's Ethical AI guidelines. The research highlights the necessity for lawful, ethical, and robust AI, prioritizing transparency, accountability, fairness, and human oversight, and explores innovative governance structures that bring together regulators, industry, and civil society. This study offers logical insights for cultivating adaptive, dynamic governance that mitigates the risks linked to generative AI while maximizing its capacity for advantageous outcomes, through an examination of prior policy documents and exemplary practices.

Paper Number

1580

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Aug 15th, 12:00 AM

Shaping Trustworthy Generative AI: Ethical Principles and Governance Frameworks

The rapid growth of generative artificial intelligence (GenAI) means that we need strong ethical guidelines and governance frameworks to build trust, stop harm, and make sure that new ideas fit with the values of society. This paper summarizes the underlying theories and established international frameworks that inform the development of dependable generative AI. It analyses emerging trends, underscores critical issues, and evaluates mechanisms for implementing ethical principles in generative AI systems by reviewing governance initiatives, including the NIST AI Risk Management Framework, the European Union's Ethics Guidelines for Trustworthy AI, and corporate commitments such as IBM's Ethical AI guidelines. The research highlights the necessity for lawful, ethical, and robust AI, prioritizing transparency, accountability, fairness, and human oversight, and explores innovative governance structures that bring together regulators, industry, and civil society. This study offers logical insights for cultivating adaptive, dynamic governance that mitigates the risks linked to generative AI while maximizing its capacity for advantageous outcomes, through an examination of prior policy documents and exemplary practices.