This page outlines how to cite AI tools and points to consider, especially for research articles. For students, we recommend the same citation and disclosure tips, but be sure to check with your instructor about AI policies for your class!
Overall publishers and institutions are still setting policies around citing and disclosing use of AI, but you will need to do one or both in your publications (and it is good practice to do so). See the other tabs and this guide on citation styles for more information too!
The author generated this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication (OpenAI Publication Policy, 2022)
As we continue to work with schools, districts, and non-profits to create GenAI guidelines for students and teachers, our approach continues to evolve. Along these lines we have updated our Student GenAI Use Guide to incorporate two major shifts in our guidance:
While MLA or APA citations are a great start, we think there is need for a new approach for GenAI use. So we are advocating for schools to ask students to provide a simple Statement of GenAI Use to describe and justify their use of these tools to augment, not replace thinking. We will be sharing a resource this week that delves deeper into this strategy.
As generative search engines like Perplexity continue to improve, they provide a great opportunity for students to be able to use GenSearch for research purposes. These tools not only hallucinate less, but provide direct links to citations along with multimedia sources.
To ensure that ACM’s Policy on Authorship is consistent with best practices and international publishing standards, ACM has become an active member of the Committee on Publication Ethics (COPE) and is committed to ensuring that ACM’s Policy on Authorship is generally consistent with COPE’s definition of authorship
Here in we propose the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative to enable cross-discipline consensus on accountable use, disclosure, and guidance for reporting of GAI/GPTs/LLM usage in academia.
Before deciding to use genAI tools in your research, check with your research supervisor (HDR students) or your School's Research Lead (staff) to make sure that it’s permissible, and that your intended use won’t breach the University’s and external research codes and policies. You may also need to consider any risks to research integrity using AI tools may introduce.
Just because information is available on the Internet does not mean that it’s free from copyright, or that it has been shared with the owner’s consent. It’s also almost impossible to know where the information came from and it could contain inherent biases, which may then be incorporated into a genAI tool’s response to your prompts. GenAI can also generate false information called 'hallucinations'. This occurs when AI either provides false information or creates what it thinks is real information but is in fact not.
For these reasons, it’s important to check the outputs from genAI to ensure you aren’t breaching copyright, consent, or research integrity – and to ensure that your research output is not flawed by bias or inaccuracy. For more information, see Issues and Considerations.
If you are permitted to use generative AI tools for any part of your research, you must acknowledge this openly in your research documentation. Use the following links to check different publishers’ statements on the use of generative AI tools to produce content for publication:
Some tools provide sources directly, while others can provide summaries to asked questions with linked sources, or lists of sources. Because of their internet connectivity and search capability, these tools are less likely to 'hallucinate' sources. With that said, it's still important to consider which of the sources provided will be suitable for your needs, as you may also be presented with general web material and grey literature in addition to articles, chapters and papers (depending on the tool used). Some recent research has suggested that the results from some tools may not be great for accuracy or understanding.
Using AI in academic research means strongly committing to transparency and accountability to keep your scholarly work legitimate (and eventually, as funded researchers, to comply with grant and publishers’ requirements). So, while generative AI can be a game-changer, it’s crucial to use it wisely and follow ethical guidelines to ensure your research is reliable and credible.
It's important to check each citation that is provided in outputs from genAI tools, due to the potential 'hallucination' problem. If you have citations taken from answers generated by genAI tools and can't find them, try searching directly for the journal titles cited to source the articles - it's possible they don't exist.
'Connector' AI function by the use of functionality known as 'citation chaining' to find cited, similar, or related articles. This is most helpful when you have found an article that is highly relevant to your needs, and you're searching for connected publications. These AI tools usually work via entering the details of an article, and can often provide visual maps that allow you to engage with other publications via their connect to the article you entered the details for.
While there are many options to enter the details of an article to start the search for connections, best practice is to enter the title or DOI of an article. This relies on materials that are openly available on the web and removes the need to upload files. Wherever possible, uploading files to AI tools should be avoided due to copyright considerations.
Artificial Intelligence (AI), a term introduced by the esteemed Stanford Professor John McCarthy in 1955, was originally described as "the science and engineering of making intelligent machines." Initially, AI research centered on programming machines to demonstrate certain behaviors, like playing games. However, the current focus is on crafting machines with the capacity to learn, resembling aspects of human learning processes.
With the proliferation of AI tools, graduate students often ask themselves how these tools can be ethically used to bolster their research. ChatGPT has gained traction, but its utility is constrained by issues like outdated information, artificial hallucinations, and limited access to scholarly publications hidden behind a paywall. Fortunately, new AI tools have been developed that promote academic exploration and discovery, such as offering generative article summaries, citation chaining, and thematic literature mapping capabilities. This workshop will explore three AI tools—Perplexity, Research Rabbit, and Inciteful—designed to support the research and literature review process. This session will provide a demonstration showcasing their features, ethical usage examples, and guidance and strategies for evaluating AI tools for research in the future.
AI For WorkAI powers driverless car research, spots otherwise invisible signs of disease on medical images, finds an answer when you ask Alexa a question, and lets you unlock your phone with your face to talk to friends as an animated poop on the iPhone X using Apple’s Animoji. Those are just a few ways AI already touches our lives, and there’s plenty of work still to be done. But don’t worry, superintelligent algorithms aren’t about to take all the jobs or wipe out humanity.
The current boom in all things AI was catalyzed by breakthroughs in an area known as machine learning. It involves “training” computers to perform tasks based on examples, rather than relying on programming by a human. A technique called deep learning has made this approach much more powerful. Just ask Lee Sedol, holder of 18 international titles at the complex game of Go. He got creamed by software called AlphaGo in 2016.
There’s evidence that AI can make us happier and healthier. But there’s also reason for caution. Incidents in which algorithms picked up or amplified societal biases around race or gender show that an AI-enhanced future won’t automatically be a better one.
Artificial intelligence as we know it began as a vacation project. Dartmouth professor John McCarthy coined the term in the summer of 1956, when he invited a small group to spend a few weeks musing on how to make machines do things like use language.
He had high hopes of a breakthrough in the drive toward human-level machines. “We think that a significant advance can be made,” he wrote with his co-organizers, “if a carefully selected group of scientists work on it together for a summer.”
Early work often focused on solving fairly abstract problems in math and logic. But it wasn’t long before AI started to show promising results on more human tasks. In the late 1950s, Arthur Samuel created programs that learned to play checkers. In 1962, one scored a win over a master at the game. In 1967, a program called Dendral showed it could replicate the way chemists interpreted mass-spectrometry data on the makeup of chemical samples.
As the field of AI developed, so did different strategies for making smarter machines. Some researchers tried to distill human knowledge into code or come up with rules for specific tasks, like understanding language. Others were inspired by the importance of learning to understand human and animal intelligence. They built systems that could get better at a task over time, perhaps by simulating evolution or by learning from example data. The field hit milestone after milestone as computers mastered tasks that could previously only be completed by people.
Deep learning, the rocket fuel of the current AI boom, is a revival of one of the oldest ideas in AI. The technique involves passing data through webs of math loosely inspired by the working of brain cells that are known as artificial neural networks. As a network processes training data, connections between the parts of the network adjust, building up an ability to interpret future data.
Artificial neural networks became an established idea in AI not long after the Dartmouth workshop. The room-filling Perceptron Mark 1 from 1958, for example, learned to distinguish different geometric shapes and got written up in The New York Times as the “Embryo of Computer Designed to Read and Grow Wiser.” But neural networks tumbled from favor after an influential 1969 book coauthored by MIT’s Marvin Minsky suggested they couldn’t be very powerful.
Not everyone was convinced by the skeptics, however, and some researchers kept the technique alive over the decades. They were vindicated in 2012, when a series of experiments showed that neural networks fueled with large piles of data could give machines new powers of perception. Churning through so much data was difficult using traditional computer chips, but a shift to graphics cards precipitated an explosion in processing power.
In one notable result, researchers at the University of Toronto trounced rivals in an annual competition where software is tasked with categorizing images. In another, researchers from IBM, Microsoft, and Google teamed up to publish results showing deep learning could also deliver a significant jump in the accuracy of speech recognition. Tech companies began frantically hiring all the deep-learning experts they could find. It's important to note however that the AI field has had several booms and busts (aka, “AI winters”) in the past, and a sea change remains a possibility again today.
Alphabet-owned DeepMind has turned its AI loose on a variety of problems: the movement of soccer players, the restoration of ancient texts, and even ways to control nuclear fusion. In 2020, DeepMind said that its AlphaFold AI could predict the structure of proteins, a long-standing problem that had hampered research. This was widely seen as one of the first times a real scientific question has been answered with AI. AlphaFold was subsequently used to study Covid-19 and is now helping scientists study neglected diseases.
Meanwhile, consumers can expect to be pitched more gadgets and services with AI-powered features. Google and Amazon, in particular, are betting that improvements in machine learning will make their virtual assistants and smart speakers more powerful. Amazon, for example, has devices with cameras to look at their owners and the world around them.
Much progress has been made in the past two decades, but there’s plenty to work on. Despite the flurry of recent progress in AI and wild prognostications about its near future, there are still many things that machines can’t do, such as understanding the nuances of language, commonsense reasoning, and learning new skills from just one or two examples.
AI software will need to master tasks like these if it is to get close to the multifaceted, adaptable, and creative intelligence of humans, an idea known as artificial general intelligence that may never be possible. One deep-learning pioneer, Google’s Geoff Hinton, argues that making progress on that grand challenge will require rethinking some of the foundations of the field.
There’s a particular type of AI making headlines—in some cases, actually writing them too. Generative AI is a catch-all term for AI that can cobble together bits and pieces from the digital world to make something new—well, new-ish—such as art, illustrations, images, complete and functional code, and tranches of text that pass not only the Turing test, but MBA exams.
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