Implicaciones de la inteligencia artificial en la metodología de investigación

Abstract

This systematic review explores how advanced data analysis technologies are transforming scientific research methodology. Sources describing the transformation of various stages of the research process by these tools, from hypothesis generation to results interpretation, were examined. These technologies offer unprecedented possibilities to enhance research. They allow for the identification of complex patterns, such as subtle trends in genomic data or unexpected correlations in sociological studies, which might go unnoticed with traditional methods. Additionally, they automate repetitive tasks, freeing up time for more in-depth analysis. Significant ethical and practical challenges are highlighted. Algorithmic biases, for example, could perpetuate existing prejudices in training data, affecting the validity of results in fields such as medical or socioeconomic research. Concerns also arise regarding the privacy of study participants and the risk of exclusion for groups underrepresented in the data. The review concludes that adopting a holistic and responsible approach is imperative. In practice, this involves developing robust ethical frameworks, fostering collaboration between disciplines such as computer science, ethics, and social sciences, investing in specialized education, promoting transparency in algorithmic processes, and strengthening global cooperation through responsible data sharing and the development of common research standards.