Plagiarism in the context of AI refers to the inappropriate use, copying or imitation of another person’s AI work, such as algorithms, datasets, architectures and models, without giving credit to the original author(s). It is important to avoid this unethical practice as it undermines originality, honesty, and academic integrity in the AI research community.


Let’s say you decoratively drew a lovely picture at school, and a friend takes it, puts his name on it and shows it to everyone as his own. That’s not fair, right? Plagiarism in Artificial Intelligence is very similar. It happens when someone takes another person’s AI work—like a smart robot blueprint or game-playing strategy—and presents it as their own, without mentioning who really made it.

In-depth explanation

In the field of artificial intelligence (AI) and machine learning (ML), plagiarism generally refers to duplicating someone else’s work without giving appropriate acknowledgment or credit.

This can encompass various forms of work, including but not limited to machine learning algorithms, model architectures, data preprocessing steps, experimental results, and even entire datasets. This is a serious ethical violation in the field, as it not only discourages the progress of research but also infringes on the intellectual property rights of the authors of the original work.

In machine learning, where model-building often requires substantial computation, plagiarism can take subtle forms. For instance, re-training a model on a slightly tweaked dataset but keeping the overall methodology and application the same is frowned upon if not outright discouraged. Similarly, modification of a few hyperparameters on a copied model and presenting it without notable contributions to the original work is also considered a plagiaristic activity.

Plagiarism can also occur in the context of AI ethics, which focuses on the fairness, accountability, transparency, and security of AI systems. For example, one could simply take a fairness metric or algorithm developed by others and apply it in their work without proper attribution. The same goes for AI for good initiatives; copying frameworks aimed at leveraging AI for social good without proper acknowledgment is a form of plagiarism.

Avoiding plagiarism is crucial in AI research to maintain credibility, trust, and fairness. It’s also essential to foster a culture that values original contributions and respects individuals’ intellectual property rights. Using tools to check for plagiarism, citing sources correctly, and promoting and practicing ethical standards can reduce the occurrence of plagiarism in the AI field.

Intellectual Property, Academic Integrity, Attribution, Machine Learning (ML), Model, Training Data, AI Ethics, Algorithm, Citation, Hyperparameters, AI for Good.