Statistical physicist, Hernan Makse has predicted that AI will one day possess the ability to predict the results of an electoral college almost completely accurately.
Henry Makse works at City University of New York and runs the Complex Networks and Data Science Lab at the Levich Institute in Manhattan. His lab utitlizes AI in the prediction of elections internationally, making use of traffic from social media, specifically on Twitter, where there are over 48 million monthly active users in the United States of America.
‘We usually start one year from the election, and then we use that data to train the machine and predict the outcome of the election at the national level’, he said in a recent interview with The Independent. He also noted how AI could be used to predict state and local elections after it has organized data through geolocation. He of course said that the prediction of election results is quite complicated.
Makse is the CEO of KCore Analytics, an AI platform which hosts live election predictions and has even initiated launching efforts concerning tracing of contacts for the Coronavirus. It utilizes over a billion tweets that have been mined for all the projects it launches. According to the KCore Analytics website, it accurately predicted the election result in Argentina last year.
Makse was in the process of the release of the final predictions concerning the 2020 US presidential elections when the interview took place. He disclosed that Democrats’ candidate and ex-Vice President had a greater edge as regarding popular vote. However, regarding the Electoral College, the AI models trained by his team predicted a different result.
Makse stated that President Donald Trump has a very little advantage over Biden in the Electoral College, while Joe Biden has a very healthy lead on him in the national vote. The physicist said Trump’s edge over Biden in the Electoral College was so small that they were still trying to ‘figure out different scenarios.’
In some manners, AI has the same flaws as customary polling and surveying choices concerning elections and political campaigns all over the nation.
Makse stated that voters who resided in rural areas are unlikely to be reached by pollsters and do no often engage in social media platforms such as Twitter, which excluded them from the data.
‘In the case of traditional polls, there are certain groups that are very difficult to capture, and they are the ones who will ultimately decide the fate of the election’, he said. ‘The people in rural areas, first of all, they do no pick up the phone when they are called for polls, but also they don’t use social media, making it very difficult to predict what these people are going to do.’
Prediction of election outcomes in other countries is somewhat simple since the national votes usually decide the results. But in the US, machines and AI must learn various models for the Electoral College which occurs simultaneously with the national vote.
It is unclear whether AI will accurately predict the outcome of the presidential election next week. But Makse stated that it is certain that in the coming years, AI will be able to predict the result of an electoral college in any given circumstance.
He said that ‘the main difference with traditional polling and Artificial Intelligence is that Artificial Intelligence is always getting better and better. Every election is a new data point, even if you didn’t get it correct… you put all this information back in the model, and then it learns from very election.’