Ethical Considerations in the Deployment of Generative AI Technologies

Abstract

Generative AI technologies provide great innovation and ethical problems. Generative AI implementation ethics are examined in this chapter, including frameworks, problems, case studies, and mitigating techniques. AI deployment ethical concerns may be solved using utilitarianism, deontological ethics, virtue ethics, and rights-based methods. Bias and fairness, privacy and data security, manipulation and misuse, openness and explainability, accountability and liability, authenticity and trustworthiness, cultural sensitivity and representation, long-term societal repercussions, and global equity and access are major ethical issues that have been identified. Deepfake technology, media and entertainment content production, healthcare AI, and legal and regulatory compliance are examples of generative AI deployment’s ethical consequences. Responsible AI innovation can be promoted by algorithmic audits, privacy-enhancing technologies, user education, ethical norms, and collaborative governance models. To address ethical issues in generative AI deployment, future strategies and recommendations involve monitoring trends, assessing impacts, addressing regulations, and prioritizing research.

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