Center for Addiction and Artificial Intelligence Research (CAAIR)
Center Overview
Every year, nearly 29 million Americans are affected by harmful alcohol use (HAU), alcohol use disorder (AUD) and substance use disorder (SUD).
The rapid innovation and transformative potential of AI present a timely and strategic opportunity to reshape how behavioral health challenges such as HAU and other substance use are understood and addressed.
- The Center for Addiction and Artificial Intelligence Research aims to facilitate the responsible integration of AI tools to understand and address the problem of addiction.
- UB’s CAAIR is a nexus for scientists, practitioners, community members, and other stakeholders to work together on AI applications of addiction research and treatment.
On This Page
By the Numbers
- Among people 12 or older 48.4 million had a past year SUD (SAMHSA, 2024)
- Among people 12 or older 27.9 million had an alcohol use disorder (NSDUH, 2024)
- 57.9 million people report binge drinking (5+ drinks in a drinking episode) in past month (SAMHSA, 2024)
- Annual economic impact of substance misuse is $249 billion for alcohol misuse and $193 for illicit drug use (Addiction and Substance Misuse Reports and Publications, 2019)
Barriers to Treatment: Poor Detection and Under-Utilized Interventions
- Only a small percentage of individuals who engage in problem substance use are identified as having a problem or receive treatment
Opportunity for Innovation
- New methods and approaches necessary to expand the reach of both assessment and intervention of HAU, AUD and SUD.
Underrealized Potential of AI
Addiction science has been slow to leverage AI’s potential and adopt AI tools.
- AI can analyze vast datasets to detect patterns, make predictions, and solve problems at a greater scale and speed than traditional computing tools.
- AI enables rapid data integration for identifying, classifying, and intervening in harmful substance use.
- AI is also susceptible to biases.
The mission of the University at Buffalo’s Center on Addiction and Artificial Intelligence Research (CAAIR) is to reduce the harms related to alcohol and other drugs by using artificial intelligence (AI) in responsible ways that serve the public and the field, including:
- Build partnerships among researchers, clinicians, and community members to understand the promise and perils of AI in addressing harmful drug and alcohol use
- Apply AI methods to advance public policy and to inform prevention and intervention
- Support and disseminate innovative research that promotes health and well-being across all stages of addiction, especially in historically underserved communities
CAAIR will:
- Unify interdisciplinary expertise and a spectrum of AI methodologies to develop sustained, scalable and adaptable solutions
- Build a cohesive infrastructure to create, implement and continuously refine AI-driven interventions
- Foster ongoing collaboration across diverse fields for research, community engagement and dissemination, advancing addiction or drug and alcohol science
These efforts will optimally position UB for additional external funding, including large-scale federal grants, foundation support and strategic partnerships aligned with AI and behavioral health.
Establish a Sustainable Center:
Create a dynamic, integrated center connecting AI and addiction science experts with key stakeholders
Leverage Local Strengths:
Capitalize on UB and New York State’s many assets to drive innovative research
Advance Addiction Science:
Foster innovation to advance research and improve mental health
Promote Responsible AI:
Lead the responsible use of AI in research and practice
Engage Community Partners:
Collaborate with stakeholders in developing practical AI tools
Disseminate Knowledge:
Share tools and discoveries broadly with the scientific community and public
- Create a self-sustaining infrastructure that will bring investigators, trainees and community stakeholders together to garner strong extramural support, create training opportunities and build UB’s national reputation as an innovator in AI-based research and addictive behaviors
- Advance the development and coordination of early-stage research projects applying AI to key challenges in alcohol science: (e.g., family-based risk detection, real-time AI-derived personalized app-based intervention and acute tolerance modeling)
- Leverage UB’s Empire AI infrastructure to: (1) train and test scalable, high-performance models in the prevention of harmful alcohol and drug use; and (2) optimization of wearable devices (e.g., watches, wristbands, etc.) that can provide personalized risk information to be used in just-in-time adaptive interventions
- Generate preliminary and proof-of-concept data to support submissions to NIH and other federal agencies
- Train UB-based scientists in the application of AI to content-area research
- Build and test flexible models for delivering AI expertise, consultation and computational support to emerging substance use research projects
- Initiate development of the AI and Addiction Research Field Guide—a public-facing, modular resource to guide responsible AI practices across research and implementation contexts
- Explore and begin to define internal processes for assessing the design, implementation and impact of AI tools, informed by input from researchers, community members and other stakeholders
- Initiate conversations with stakeholders—including researchers, practitioners and community members—to better understand priorities, expectations and opportunities for meaningful engagement
- Begin assembling a group of community-based advisors (community advisory board; CAB) to provide ongoing input on project direction, communication strategies and relevance to diverse audiences
- Identify potential audiences and formats for sharing early insights and tools, with attention to accessibility, usability, scalability and real-world application
- Lay the groundwork for a future dissemination strategy that reflects principles of transparency, reciprocity and inclusive knowledge sharing
CONTACT US:
Lynn Homish, MS
dlhomish@buffalo.edu