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AI Helps Us Better Understand Voter Intent

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Artificial Intelligence Help Understand Voters

AI Helps Us Better Understand Voter Intent

AI Helps Us Better Understand Voter Intent

Understanding the electorate has become more and more difficult over the past several elections. There are multiple reasons for this, including a change in the people answer phone calls from unknown pollsters, to historical voting trends being upended across the nation. Whatever the reason, pollsters have famously failed to predict voting outcomes as of late. Researchers are now looking to AI for help in better predicting the electorate’s sentiment and ultimately, accurately predicting election outcomes.

Artificial Intelligence Help Understand Voters

In direct response to the failure of traditional polling, researchers are experimenting with the same technology companies use to predict their customer sentiment, and applying it to elections. Oren Etzioli, CEO of the Allen Institute of AI, suggest that he would not, “fire the pollsters, but I would direct them to try to leverage machine learning, data mining and AI in their work more to get better projections.”

In order to make use of machine learning to better understand voter preferences, pollsters are recommended to approach the problem the way a big retailer would approach building a customer profile. Rather than replying on historical voting patterns and adjusted averages, AI can better understand voter intent by examining millions of social posts and other information sources for patterns and correlations that provide deeper insight into the electorate.

In order to make use of machine learning to better understand voter preferences, pollsters are recommended to approach the problem the way a big retailer would approach building a customer profile. Rather than replying on historical voting patterns and adjusted averages, AI can better understand voter intent by examining millions of social posts and other information sources for patterns and correlations that provide deeper insight into the electorate.

Perhaps most interestingly, AI does not need necessarily need to examine millions of data pieces to make a prediction. Well-built AI models can significantly reduce the sample size required for accurate predictions. Unanimous, and AI-focused company, was able to outperform traditional polling with a sample size of just 50 voters, rather than the tens or hundreds of thousands of data points typically used to predict election outcomes.

While there is no end to traditional polling in sight, these young AI models only stand to improve over time. As time goes by and these models ingest more and more data, their elections predictions are expected to improve. And that’s something that everyone can get behind.