Human-centered artificial intelligence for a sustainable future
The field of artificial intelligence presents wide ranging opportunities for innovation, education, sustainable growth, and community engagement. As top researchers bring future AI innovations into our daily lives, a well-rounded education pairs these advances in science with the complex ethical questions necessary to foster responsible use. At UMass Amherst, you'll find opportunities to learn about and engage with AI that connect your critical thinking with your creativity.
Guidance and Support
In academic year 2023/2024, the Joint Task Force on Generative AI worked to develop a set of shared values and recommendations regarding Generative AI (genAI) on campus. These values and recommendations emphasized the importance of preserving the human-to-human interaction that is at the core of our education and research endeavors while also supporting the thoughtful application of new technologies, like generative AI (genAI). Implementing these recommendations involves developing guidance and education for students, faculty, and staff in genAI literacy, including responsible genAI use and a critical understanding of its limitations. Read the final task force report to learn more: Special Report of the Joint Task Force on Generative AI. UMass IT provides faculty and staff access to an AI Platform, which gives campus community members access to exploring different generative AI models.
What is Generative AI?
Stated simply, generative artificial intelligence (GAI) is a system that relies on very large data sets to generate content (text, images, video) in response to a prompt. This generated content is composed based on a complex model that predicts the likelihood of what output best matches the words used in the prompt. That model uses a variety of computational methods to associate likely outputs with the prompts entered by the user. This model is based on a representation of the contents of the large data set, and not based on a ‘human’ understanding of the prompt or the content it generates. The collections of texts and images and video informing most commercially available generative A.I. software are taken primarily from the internet and from the prompts entered by users. As a result, the quality of GAI output varies in terms of accuracy and bias.
Generative AI and Data Privacy
The UMass Joint Task Force on Generative AI agreed that genAI use "must adhere to legal and university policies on data privacy and compliance, such as, but not limited to, academic integrity, the BOT Policy on Responsible Conduct of Research and Scholarly Activities, FERPA, HIPAA, and IRB protocols." It is important that all members of the university community familiarize themselves with the data privacy implications of genAI tools and technologies.
Generative AI and Academic Research Writing
Undergraduate and graduate students engage in academic research writing during their UMass careers. It is important to learn when and how to use genAI in research writing so that students can preserve their authentic voice and sustain their academic learning. GenAI tools may not always be appropriate for academic research writing tasks, but there are times when they can be useful when used responsibly. Watch the video to learn more.
UMass Graduate Students Discuss their AI Research
Generative AI for languages besides English?
Manning College of Information and Computer Sciences Ph.D. Student, Marisa Hudspeth discusses her research into using large language model technologies for Latin.
Computational biology as a research tool for improving disease treatments?
Manning College of Information and Computer Sciences Ph.D. Student Mahbuba Tasmin discusses her research using machine learning models for predicting antibiotic resistance in treating disease.
Large language models as tools for studying social movements online?
Manning College of Information and Computer Sciences Ph.D. Student Tessa Masis discusses their research using large language models to help in analyzing attitudes and ideological movements on social media.
Studying AI at UMass
What to learn more about artificial intelligence? UMass Amherst offers multiple certificate, undergraduate, and graduate degree programs in areas relevant to AI.
Professor Joseph Pater and Data Scientist Virginia Partridge, Public Interest Technology Fellows, discuss their ongoing research project applying large language model technology to create automatic transcription into the International Phonetic Alphabet. Joseph co-directs the Computational Phonology Laboratory at UMass which aims to facilitate research on computational phonology by making research and tools accessible to members of our community and others. Virginia manages the Data Science for the Common Good internship program at the Center for Data Science. She designs and evaluates AI, ML & NLP systems and experiments, ensuring that they align with users' needs in context and building actionable workflows for managing, annotating and analyzing data.
Assistant Professor and computational biology researcher, Anna Green discusses her work on antibiotic resistance in treating tuberculosis. She directs the Sequence Analysis and Genomics (SAGE) lab at CICS, where computational methods are built and applied to understand genetic variation. Asst. Professor Green's work has a particular emphasis on antibiotic resistant bacteria, which pose a major and evolving public health threat.
Associate Professor of Computer Science, Brendan O'Connor, shares insights about his natural language processing and the importance of building increasingly more accurate and reliable large language models. He works in the intersection of computational social science and natural language processing (NLP) — studying how social factors influence language technologies, and how to better understand social trends with text analysis. In asking what can statistical text analysis tell us about society? The SLANG Lab (Statistical Social Language Analysis, directed by Prof. Brendan O'Connor) develops natural language processing, machine learning, and data analysis tools to improve scientific investigation about political and social phenomena.
More AI Research
As one of the top destinations for AI research in the world, UMass is constantly pushing the state-of-the-art with ongoing research programs that combine AI with personal health and wellness solutions, sustainability, social media analysis, and robotics.
There are a wealth of resources available to faculty seeking to integrate GenAI into their classes. The Center for Teaching and Learning and IDEAS have consultants ready to support instructors who have questions about how they might explore adding AI into their teaching.
The Humanities and Fine Arts - AI and Emerging Technologies Committee
The Manning College of Information and Computer Sciences - AI Safety Initiative
The CICS AI Safety Initiative advances research to ensure the safe, secure, and ethical development of artificial intelligence. Contact co-leads Shlomo Zilberstein and Eugene Bagdasarian for more information.
Responsible AI PIT Fellows
The Public Interest Technology Initiative at UMass is pleased to announce the recipients of its 2025-2026 Faculty Fellowship. Fellows will receive seed funding to support research, scholarly writing, or curriculum development on the theme of Responsible AI – how we create, use, and manage AI responsibly to promote the common good and public interest. Learn more.
College of Social and Behavioral Sciences - AI and Social Science Series Planning Group
Targeting the social and behavioral sciences community, including alumni and friends of the college, this group works to plan AI-related and disciplinarily relevant activities and events supporting innovation and responsible use.
Important considerations for Students, Faculty and Researchers
University contracted tools, like CoPilot, and the UMass IT provided AI Platform enable exploration of generative AI technology while protecting our data. Please note that the AI Platform has content filtering enabled, and that service may impact prompt results. You can read more about the specific content filtering service being used here. Our outstanding libraries are leading information literacy education by helping students and researchers acquire state of the art skills for searching for and evaluating information.
Classes help train graduate and undergraduate students in the use, application, critical evaluation, social impacts, and inner workings of AI technologies. Generative AI tools can be powerful and exciting to use, and they can make mistakes.
Students, researchers, and staff should consider expectations for responsible use of AI for their work at UMass, including academic honesty, data privacy, and accountability.