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Penn Staters Challenged to Detect Bias in AI Tools

Penn Staters Challenged to Detect Bias in AI Tools
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Amulya Yadav , Penn State Center , Penn State , Socially Responsible Artificial Intelligence ,

AI can help reduce risk of HIV in high-risk communities


AI can help reduce risk of HIV in high-risk communities
How to get good information to the people who need it is a question that has long plagued public health officials.
One approach, known as peer change agents, is to recruit peer leaders to promote healthy behaviors and information about disease prevention within their social networks. This strategy has been used, with mixed results, in communities at high risk for HIV infection and transmission, specifically among young people experiencing homelessness.
Youth experiencing homelessness are 10 times more likely to test positive for HIV than young people who have access to stable housing.  Social workers and public health officials have used the peer change agents strategy to promote behaviors such as condom usage and regular HIV testing within these communities, but success seems tied to choosing the right peer leaders who will have the largest impact within their communities. ....

United States , University Of Southern California , Laura Onasch Vera , Chyna Hill , Amulya Yadav , Bryan Wilder , Milind Tambe , Graham Diguiseppi , Adaptive Network , Career Development Assistant Professor , Artificial Intelligence In Society , Association For The Advancement Of Artificial Intelligence , Robin Petering Of Lens Co , Harvard University , College Of Information Sciences , Army Research Office , Research Program , Harvard Johna Paulson School Of Engineering , Penn State , Information Sciences , Southern California , Gordon Mckay Professor , Computer Science , Harvard John , Applied Sciences , Development Assistant Professor ,

AI-defined COVID-19 testing strategy could lead to fewer infections


AI-defined COVID-19 testing strategy could lead to fewer infections
When the novel coronavirus pandemic spread across the globe, governments and institutions worldwide faced hard decisions about who to test for the virus and when with limited testing supplies.
Now, a new algorithm developed by researchers at Penn State’s College of Information Sciences and Technology could help leaders make better informed decisions on how many symptomatic and asymptomatic individuals to test with rationed daily tests, and at what stage of the pandemic. The model’s simulated testing strategies resulted in approximately 40% fewer infections.
“Our goal was to find out how do you distribute an allocation of tests that you have every day,” said Amulya Yadav, PNC Technologies Career Development Assistant Professor at the College of IST. “How do you distribute them among symptomatic and asymptomatic people? And how should this allocation change over time?” ....

Lyu Liang , Amulya Yadav , Penn State College Of Information Sciences , Technologies Career Development Assistant Professor , International Conference On Autonomous Agents , Penn State , Information Sciences , Career Development Assistant Professor , Partially Observable Markov Decision Processes , International Conference , Autonomous Agents , Multiagent Systems , யூ லியாங் , அமுல்யா யாதவ் , பென் நிலை கல்லூரி ஆஃப் தகவல் அறிவியல் , தொழில்நுட்பங்கள் தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , பென் நிலை , தகவல் அறிவியல் , தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , சர்வதேச மாநாடு ,