University of Denver Winter 2024

reduction in deviancy training over the randomly assigned groups. Barman-Adhikari previously used AI to predict substance use based on conversations and posts from Facebook feeds. The AI-powered algorithm was 80% accurate in predicting an individual’s substance use compared to approximately 30% using traditional statistical models. For Barman-Adhikari, AI is shaping up to be a tool for triage, allowing researchers and social workers to maximize the effectiveness of the limited resources available to them. With promising results from this and other recent research, Barman Adhikari says that if researchers and policymakers heed caution in the development and use of new AI tools, they will be better able to avoid reproducing harmful societal biases and other consequences. “Some of the fears are legitimate,” she says. “I think we need more awareness of what the technology can do. We also need to advocate for better

predict air quality across the Denver metro area. Combining a network of low-cost air quality sensors with machine learning techniques including graph embedding to track spatial data; long short-term memory to account for time; and neural networks to integrate environmental and societal factors, the system allows for considerably more up-to-date tracking and prediction of air quality and is far more geographically precise. Its impacts are immediate. “Predicting PM2.5 concentrations helps individuals—particularly those with respiratory diseases or long COVID— take precautions to reduce exposure, ultimately leading to better health outcomes,” she says. In her previous research, Li also developed a model simulating the spread of COVID-19 among neighborhoods. YOUTH SUBSTANCE USE AND HOMELESSNESS INTERVENTIONS In addition to tracking and pre dicting environmental and biological events, DU researchers are finding that AI can be useful in tackling social problems. Anamika Barman-Adhikari, associate professor in the Graduate School of Social Work, developed a machine learning system that assists social workers in designing group drug-use interventions for youth experiencing homelessness. Such interventions are typically made up of peer-led groups that are randomly assigned and, at times, result in deviancy training—when individuals learn and reproduce harmful behaviors from their peers. By mapping individuals’ networks of relationships and behaviors, Barman Adhikari’s AI system simulated the outcomes for each potential group configuration and selected the group with the best outcomes. The AI assisted groups showed nearly a 60%

interdisciplinary teams of researchers, developers and users alike to consider where training data comes from, what societal patterns will be reflected in the data and the effects that an AI’s implementation has on real people. Fears that AI will destroy the world and end life as we know it are, fortu nately, not totally realistic—at least not with existing technological infrastructure, Haag says. But AI will have significant impacts on education, work, transportation, medicine and most other fields. “I look at it from three points of view: efficiency, effectiveness and innovation. And I think it’s innovation in the space that has a lot of people really frightened,” he says. “We’re going see a transition in a lot of job areas where AI is going to be able to take over some aspects of it, but not all,” he says. And for Haag, the potential for AI-enabled smart homes, tailor-made educational software and break throughs in health care and medical research—like DU researchers’ ongo ing application of AI to air pollution, infectious disease and substance use interventions—is far more exciting than worrisome. MODELING REAL-TIME AIR QUALITY From respiratory diseases to cardiovascular issues and premature death, exposure to fine pollutant particles in the air has serious conse quences. While air quality is tracked by organizations including the Envi ronmental Protection Agency, the data often covers wide areas and is updated daily, at best, making it challenging for vulnerable populations to protect themselves as air quality fluctuates from neighborhood to neighborhood from hour to hour. Recently, Jing Li, associate professor in the Department of Geography and the Environment, used machine learning to develop a precise, real-time system to track and

regulation. But the silver lining is that if this technology is used wisely, I think it can radically

change our lives for the better.”

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