Explore generative AI on JSTOR

By incorporating generative AI features into the JSTOR platform, we aim to equip students, faculty, researchers, and librarians with innovative tools that facilitate engagement with complex content and enrich research and learning. This early release harnesses the power of generative AI to offer the following capabilities:

• Generate a summary with key points and arguments from the text itself, helping users quickly determine if content is relevant to their research
• Suggest topics and show related content within the JSTOR corpus that is relevant to the text, enabling exploration of additional possible paths of inquiry
• Answer questions posed by users based only on the content of the document being viewed
• Search JSTOR in a new way with a semantic search-powered capability that works better for natural language queries than traditional keyword search

JSTOR has previously applied machine learning and artificial intelligence technologies to optimize the research experience. For example, we have created a citation graph to link all articles on JSTOR, and used machine learning to improve the relevance of search results and recommendations. As we extend our knowledge and application of new technologies to generative AI, we expect to iterate and evolve as we learn. By volunteering for our limited beta test, you will help us define the long-term scope of this exciting new initiative.

At present, the tool can be used with journal articles, book chapters, and research reports found on JSTOR. Images and text-based primary sources on JSTOR are not yet included.

To start, we are using only the contents of the document being viewed to generate responses. Over time, as we learn from and improve upon the accuracy of responses, we might extend this to use the content of other relevant documents in the JSTOR corpus.

To jumpstart the learning and experimentation process, the beta release of our generative AI-powered tool uses gpt-3.5-turbo from OpenAI and the open source all-MiniLM-L6-v2 sentence transformer model. We are actively exploring alternatives and expect to evolve the models we use as our environment develops.

We are monitoring and continuously improving accuracy using the following methods:

• Subject matter experts from a range of academic disciplines conduct in-depth, ongoing evaluations of the tool’s output. These evaluations help us ensure that generated content is useful and accurate.
• Users provide in-tool feedback that we use to identify areas for improvement. All interactions with the beta tool offer users the opportunity to provide a thumbs up or thumbs down rating. For thumbs down ratings, the user can provide further detail to explain their response.
• We assess model performance for our specific use cases using industry-standard metrics for Machine Learning (ML) and Natural Language Processing (NLP). Additionally, we continuously integrate new evaluation metrics specifically developed for Large Language Model (LLM) use cases.

JSTOR handles all personal information, including information provided to this tool, in accordance with our privacy policy. JSTOR does not sell user data, nor does it share content or user data from its platform for the purposes of training third-party large language models.

Any data you provide to this tool, such as question prompts and other conversation data, will be stored in JSTOR’s internal systems and used in de-identified form to maintain and improve the tool. Your prompts and some/all of the text of the content being viewed is also sent to OpenAI to generate the response. OpenAI does not use this to further train their models, nor do they retain the data for more than 30 days, in accordance with OpenAI’s API data usage policies.

Your data will be used in this way only if you opt in to the beta testing program.

As a nonprofit service, JSTOR’s financial model is designed to recover our costs and support sustainable growth to meet the emerging needs of the education community. As we learn more about what it costs to build and maintain generative AI on our platform, we will evaluate how to bring these powerful capabilities forward as equitably and sustainably as possible.

JSTOR maintains physical, technical, and administrative safeguards to protect the content we hold. We are a SOC2 compliant organization whose data security practices and measures are audited annually by independent third parties.

Content is only processed internally and with the OpenAI API. Please note the OpenAI API only temporarily stores data for the purpose of processing and does not use such submitted data to train their models or improve their service offering. For more information on OpenAI’s data security practices, please consult the OpenAI Trust Portal.

Technology has always been an incredible accelerator for ITHAKA’s mission to improve access to knowledge and education. As a trusted provider of scholarly materials, we have a responsibility to leverage our content, technology, and deep subject matter expertise to chart a path forward that makes the use of AI safe, effective, and affordable for our constituents.

• We honor our values first and foremost. JSTOR provides users with a credible, scholarly research and learning experience. Generative AI must enhance that credibility, not undermine it.
• We will listen closely and proceed cautiously. We recognize the concerns associated with generative AI and are pursuing this work mindful of the very real considerations at hand. Our first step is to deepen our collective understanding through research and by doing – and as always, in close collaboration with our community. We will use these tools safely and well.
• We empower people, we do not replace them. These tools should not be used to “do the work.” They should be designed to help people, especially students, learn and do their work more efficiently and effectively.
• We will enable our systems to interact with users in ways that are comfortable. Traditionally, it has been the users’ responsibility to adapt to restricted language and structures to provide computers with inputs; computers can now interact effectively with users in natural language, and we should take advantage of that.
• We will lead with care. We will deeply consider the aspirations and trepidations of the many communities we serve.

As we learn about and pursue this latest technology, we look forward to your engagement and insights to ensure we continue to deliver high-quality, trusted, impactful services that improve access to knowledge and education for people everywhere.