Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education

Area of Science:

• * Computer Science
• * Ethics
• * Nursing

Background:

• * Growing recognition of artificial intelligence (AI) ethical concerns, including bias, opacity, trust, security, and fairness.
• * Machine learning (ML) algorithms are central to AI, necessitating ethically sound systems.
• * Data is crucial for AI, emphasizing the need for ethical data collection, processing, and usage.

Purpose of the Study:

• * To explore data ethics principles for responsible AI development.
• * To examine the data-centric AI paradigm and its focus on data quality.
• * To address ethical considerations for nurses in AI and data ethics.

Main Methods:

• * Formal literature review to identify relevant data ethics frameworks and strategies.
• * Examination of principles applicable to real-world and synthetic data in AI/ML.
• * Analysis of ethical considerations specific to nursing practice, research, and education.

Main Results:

• * Data ethics principles are essential for responsible AI and ML.
• * The data-centric AI paradigm highlights the importance of data quality and human-centered expertise.
• * Specific ethical considerations and case studies for nurses in AI are presented.

Conclusions:

• * Nurses should actively engage in AI and data ethics.
• * This engagement is crucial for developing high-quality, relevant data for ML applications.
• * Recommendations are provided for nursing practice, research, and education to foster ethical AI integration.