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American University: Using Statistics to Aid in the Fight Against Misinformation

#artificialintelligence

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics in the College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.


How statistics can aid in fight against misinformation

#artificialintelligence

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.


How statistics can aid in the fight against misinformation: Machine learning model detects misinformation, is inexpensive and is transparent

#artificialintelligence

With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans. "We would like to know what a machine is thinking when it makes decisions, and how and why it agrees with the humans that trained it," Boukouvalas said. "We don't want to block someone's social media account because the model makes a biased decision."


Microsoft announces AI for Earth to help the planet with machine learning

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Hot off Google unveiling its own initiative for improving artificial intelligence's impact on humanity, fellow AI research giant Microsoft has announced a program dedicated to improving the planet through machine learning. AI for Earth, revealed during Microsoft's AI event today in London, will assist organizations using AI for environmental protection, innovation and research -- particularly those addressing issues in water conservation, agriculture, biodiversity and climate change. According to Microsoft, AI for Earth will operate on three major "pillars" -- granting access to Microsoft's resources for research groups, providing educational resources to teach said groups how to utilize AI optimally and special lighthouse projects that innovate AI's ability to study the environment. To that effect, Microsoft also announced today it will invest $2 million into AI for Earth, which will manifest as research grants enabling access to its cloud and AI tools, as well as technical training on its various platforms. Though a newly announced initiative, Microsoft has already demonstrated concepts for what AI for Earth could accomplish, including past work where it's used machine learning and cloud computing to monitor watershed in the Chesapeake Bay, track soil moisture levels, and power Project Premonition -- a multi-step program that aims to analyze and prevent mosquito-borne disease outbreaks.