Integrating Artificial Intelligence into Research Methodology: Examining Potential Bias and Mitigation Strategies

Abstract

This study represents a significant contribution to understanding the challenges and opportunities associated with using artificial intelligence in scientific research, with a particular focus on ethical issues and bias. In recent years, these critical issues have been increasingly addressed, as highlighted in previous studies that shed light on their importance. One of these studies is the research by Amini and colleagues (2023), who discussed the negative impacts of bias in artificial intelligence on research outcomes, indicating that the input data and algorithms used can lead to misleading results if not handled carefully. This underscores the need for researchers to be cautious about the integrity of their data and the algorithms they employ. On the other hand, the study by Kahn and others (2023) presented strategies to reduce bias and enhance transparency in the use of artificial intelligence in scientific research, emphasizing the importance of ethical training for researchers. By studying these issues, this research aims to address the gaps identified in previous studies and provide practical strategies that can help reduce bias and improve result accuracy. It also seeks to enhance ethical awareness among researchers and assist academic policymakers in developing training programs aimed at enhancing researchers' skills effectively and ethically via artificial intelligence techniques.