The Ethical Artificial Intelligence Framework Theory (EAIFT): A New Paradigm for Embedding Ethical Reasoning in AI Systems

Abstract

The rapid development of artificial intelligence (AI) has created several ethical issues, including bias, a lack of transparency, and privacy concerns, demanding the incorporation of ethical governance directly into AI systems. This study introduces the Ethical Artificial Intelligence Framework Theory (EAIFT), a novel approach to incorporating ethical reasoning into AI. It emphasizes real-time oversight, open decision-making, bias detection, and the ability to change ethical and legal norms. EAIFT advocates for establishing "ethical AI watchdogs" that automatically monitor and ensure the ethical operation of AI systems, together with dynamic compliance algorithms that can adapt to regulatory changes. The paradigm also encourages transparency and explainability to build user trust and detect and correct biases to ensure fairness. This paper employs a qualitative methodology that combines stakeholder interviews, content analysis, and expert commentary to evaluate EAIFT's potential to increase ethical accountability in various areas, including healthcare, banking, and criminal justice. The findings suggest that EAIFT outperforms existing ethical frameworks by proactively reducing biases, increasing transparency, and ensuring adherence to ethical standards. While presenting a comprehensive and adaptable technique, the study also acknowledges limitations in empirical testing and the need for additional research to widen EAIFT's applicability to future ethical challenges in artificial intelligence. The paper suggests future research subjects, such as empirical testing in different scenarios, a more in-depth examination of ethical risks, and the inclusion of the framework into new AI technologies to promote responsible AI governance by societal norms and values.

Keywords:

Artificial intelligence, Ethical governance, AI bias, Transparency, Privacy, Ethical artificial intelligence framework theory, EAIFT, Real-time Oversight, Ethical AI watchdogs, Dynamic compliance algorithms, Transparency in AI, Bias detection, Ethical decision-making