54 - Ethical Considerations of Artificial Intelligence (AI) Use in Research Settings

Background:  We aim to evaluate the suitability of current ethical frameworks for artificial intelligence (AI) in research, focusing on whether these guidelines adequately address the unique challenges posed by AI technologies. Discussions emphasize the need for frameworks that address bias perpetuation and the ethical implications of AI's unpredictable evolution in research.

Methods:  Our study employs a mixed-methods approach, integrating a comprehensive literature review with interviews from experts of diverse backgrounds to examine the effectiveness of existing ethical frameworks against AI-specific issues. Emphasizing data privacy, informed consent, and recurrent biases in AI training data, the research identifies gaps in current guidelines and proposes necessary adjustments. Theoretical analysis helps scrutinize these frameworks and generate innovative guidelines tailored to address the intricacies of AI in a research context.

Conclusion:  Integrating AI into research settings introduces ethical challenges that are only partially encompassed by existing frameworks. Among these are issues related to the transparency of algorithmic processes and the adequacy of informed consent when AI is involved in data collection. This analysis examines the effectiveness of existing ethical frameworks with the hopes of informing the development of more dynamic, transparent, and accountable frameworks. These guidelines should include specific oversight mechanisms attuned to the rapidly evolving nature of AI technologies, which are crucial for maintaining research integrity and protecting participant rights. Importantly, these frameworks must respond to current technological capabilities and anticipate future developments, ensuring they remain relevant and effective. The proposed enhancements ensure that ethical oversight keeps pace with technological advancement, facilitating a balance between innovation and ethical accountability in research. Recommendations call for establishing clear protocols for transparency in AI methodologies, mechanisms to address and mitigate inherent biases, and processes that ensure continuous participant engagement and consent. Our conclusions also integrate considerations for regular audits of AI models to combat bias and ensure ethical adaptability. By establishing these robust frameworks, the research community can foster an environment where AI aids in advancing knowledge while adhering to the highest ethical standards.

Limitations: This conceptual analysis is limited by AI technology’s rapid evolution, potentially rendering proposed guidelines obsolete. Emphasis on high-risk research settings may not seamlessly translate to lower-risk or non-academic environments. Considering inherent biases in AI design further complicates the application of ethical guidelines across different contexts. Additionally, potential counterarguments regarding the feasibility of continuously updating ethical standards and the practical challenges of implementing dynamic frameworks across diverse institutions highlight the need for adaptable and scalable solutions.

Discussion:  To ensure robust ethical oversight of AI in research, ongoing revisions of guidelines are essential. Future work should empirically validate these guidelines across various disciplines and AI applications. Importantly, IRBs should include members with AI expertise to adequately address the unique challenges such technologies pose. Additionally, enhancing IRB expertise should encompass accessibility in AI, ensuring that ethical standards also address the needs of diverse populations and prevent bias. This interdisciplinary collaboration among ethicists, AI specialists, and policymakers will be vital for proactively managing emerging ethical dilemmas, refining guidelines, and safeguarding research integrity and participant rights amidst rapid technological advancement.