Employing generative AI for your research can enhance your work in several different ways, adding efficiencies to research process and workflows including:

  • Discovering literature, summarizing key points, and trends
  • Digesting large-scale information into smaller more manageable bits of information
  • Aiding in the analysis and annotation of information
  • Authoring, refining, and editing content

However, there are several considerations to keep in mind when incorporating the use of these tools into your research process. 

Below you will find a list of some of the key areas to consider as well as some tips and common guidelines for practice:

 

Trustworthiness/Reliability: 

While many of these tools are drawing on vast amounts of reliable data, the black-box nature of these applications can at times make it difficult to verify the source of the the information they generate.  When researchers are unable to verfiy the source of the information and contents these tools produce, and the sources it was trained on can make it difficult to rely on the accuracy of the output.

  • Consider consulting with a librarian who can work with you to help fact-check and verfiy information sources. 
  • Consider consulting multiple sources of information in order to verify the outputs you are yeilding from generative ai tools.

Generative AI is NOT a research database:  

While these tools can be used for discovery, they are not a scholarly literature database nor are they a search engine. In some cases these products will have been trained using data from scholarly resources, but unlike a discovery platform which will point you directly to the source literature, generative AI tools are designed to "generate" their own content or output.

  • Consider searching for literature in actual research databases or discovery platforms like HOLLIS, when looking for new resources
  • Consider consulting with a librarian or expert in your field or discipline who can help you to both identify relevant titles and help verify their authenticity and provenance
  • Features like reliability and trustworthiness are commonly highlighted in relation to scholarly research resources. Understanding and verifying the quality of the source material in generative AI tools can commonly be a challenge.

Privacy and Bias and Other limitations: 

While some products and tools are open source others are commercial and commonly capture information about users, biases in these products are derived from the data sets used to train them. Generative AI tools commonly reproduce biases inherently found in the data sets they are trained on which can perpetuate both harm and misinformation.

Generative AI tools continue to evolve and change, with new products continually being created and introduced.  We can anticipate that some existing tools and functionalities will be discontinued, while others will develop new enhancements and versions.

As different generative AI products are trained on different large language models, the data ranges and accuracy of the included information will vary from tool to tool.  When searching for timely or current information, an AI tool will only be as good as the dates of coverage included in the data set it was trained on.

Copyright considerations

Generative AI tools are evolving quickly along with their impact on research, teaching, and learning.  Use of these tools pose a number of both opportunities and challenges to our common understanding of the application of related policies and regulations.Some of the concerns surrounding Generative AI and copyright our outlined below: 

  • Input: The legality of the content used to train AI models is unknown in some cases. There are a number of lawsuits that allege Generative AI tools infringe copyright and it remains unclear if and how the fair use doctrine can be applied. 
  • Output: Authorship and ownership of works created by AI is unclear. Most recently, the US Copyright Office has published the following guide addressing these issues: Copyright Registration Guidance for Works Containing AI-Generated Materials

If you would like to use Generative AI tools for content generation, consider the following: 

While you can use these tools to create content, you may not own or hold copyright in the works they generate

  • Be mindful of what you input into tools: avoid putting confidential information or significant portions of intellectual property you do not have the rights or permissions to into these systems. All content entered may become part of the tool’s dataset and may inadvertently resurface in response to other prompts 
  • Review the terms of service of each tool: These terms will dictate use and ownership of input/output and they are subject to change without notice 
  • Be explicit in indicating how you have used these tools in the creation of your work. Keep a record of prompts and any IP you have used in the creation of output. Review attribution guidelines according to the style guide you are using  If you are publishing your work, review any requirements or policies that address the use of generative AI tools in your research. These policies will indicate whether AI can be used and how the use of these tools should be disclosed.    https://onesearch.library.utoronto.ca/copyright/generative-ai-tools-and-copyright-considerations

 

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