The use of artificial intelligence in academic and research environments generates intense debate. On one hand, it presents itself as a tool capable of analyzing large volumes of data and suggesting hypotheses. On the other, it raises doubts about the originality of the work and the possible generation of biased or invented content. This thread explores both sides of the coin.
Language Models and Data Analysis in Research ??
Technically, AIs applied to research operate mainly as synthesis and processing assistants. LLMs can review literature, extract patterns from studies, and draft outlines. More specialized tools analyze complex datasets, identifying correlations that might go unnoticed. The critical point is validation: AI results require rigorous verification, as models can hallucinate sources or data.
My Coauthor is an Algorithm: Adventures in Ghost Authorship ?‘»
The situation is curious. Now you can have a collaborator who never sleeps, doesn't ask for grants, and whose only conflict of interest is its training bias. You write a paper and, in the acknowledgments, you're tempted to put: Thanks to GPT for not complaining about overtime. The problem comes when you try to cite it in the bibliography and can only refer to a model with 175 billion parameters. Peer review turns into an interrogation: Can your coauthor attend the conference to defend the method?. No, it can only generate excuses.