Generative AI tools (ChatGPT*) in social science research

Abstract

Purpose This paper aims to critically examine the implications of using generative artificial intelligence (AI) models, such as ChatGPT and Bard, in social science research. It examines the doppelganger effect in AI-driven studies as well as cognitive dissonance brought on by the autonomy of these tools. The discussion also addresses the debate between quantitative and qualitative methods for evaluating AI-driven research, scrutinising existing guidelines for accountability and validity. In addition, the paper considers the potential for generative AI to dominate research, identifying “non-takeoverable” skills and ethical issues in AI-driven knowledge production. Design/methodology/approach This work primarily focuses on research articles for conceptual clarity, while news media reports are used to illustrate current scenarios. Findings The doppelganger effect makes people worry about situations in which AI copies existing work so well that it becomes possible for people to give the wrong credit. This has led to a critical review of ways to make sure that the outputs of generative AI are real and original. Generative AI can enhance data collection and analysis, offering alternative approaches to traditional research methodologies. By leveraging the capabilities of generative AI, researchers can potentially uncover new insights and perspectives from their data. Originality/value It is crucial to acknowledge the ethical concerns associated with using generative AI in social science research. The deployment of such technology introduces the possibility of biases and other ethical challenges that may impact the cognitive abilities of human participants or researchers involved in the research process. The work makes an effort by encouraging ethical consideration and highlighting crucial human abilities that are still necessary, providing a novel viewpoint on the use of generative AI in research approaches.

Ähnliche Arbeiten