With all the focus on generative intelligence and the misinformation spreading, what are the real benfits and concerns for academic publishing? The Scholarly Kitchen brought this interesting topic to our attention in their article, “Who Is Going to Make Money from Artificial Intelligence in Scholarly Communications?“
There are extensive lists of benefits, but primarily for academic publishing it often involves extensive literature reviews to identify relevant research and existing knowledge gaps. Generative intelligence can assist researchers in quickly scanning and summarizing a vast amount of literature, extracting key concepts, and providing relevant information. This can save significant time and effort for researchers, allowing them to focus on higher-level analysis and interpretation.
Generative intelligence can also assist in the peer review process by identifying potential conflicts of interest, checking for plagiarism, and aiding in the evaluation of submitted manuscripts. Reviewers are also helped by suggested relevant prior work or related studies that should be considered during the review process.
It’s important to note that while generative intelligence can provide valuable support, it should be used as a tool to augment human intelligence rather than replace it.
The biggest challenge is that most organizations have little knowledge regarding how artificial intelligence (AI) systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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