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Explanation of different models related to divisive issues in the US

As the United States (along with several other countries) gears up for general elections, AI is helping campaigns attract voters with increasing sophistication.

What’s new: Strategists for both major U.S. political parties are using machine learning to predict voters’ opinions on divisive issues and using the results to craft their messages, The New York Times reported.

How it works: Consulting firms typically combine publicly available data (which might include voters’ names, ages, addresses, ethnicities, and political party affiliations) with commercially available personal data (such as net worths, household sizes, home values, donation histories, and interests). Then they survey representative voters and build models that match demographic characteristics with opinions on wedge issues such as climate change and Covid-19 restrictions.

  • HaystaqDNA, scores 200 million voters on over 120 politically charged positions. The company helped U.S. president Barack Obama during his successful 2008 campaign.
  • i360 scores individuals on their likelihood to support specific laws such as gun control, increasing the minimum wage, and outlawing abortion.

Behind the news: AI plays an increasing role in political campaigns worldwide.

  • Both major candidates in South Korea’s presidential election earlier this year used AI-generated avatars designed to connect with voters.
  • In 2020, a party leader in an Indian state election deepfaked videos of himself delivering the same message in a variety of local languages.

Yes, but: Previous efforts to predict voter opinions based on personal data have been fraught with controversy. In the mid-2010s, for instance, political advertising startup Cambridge Analytica mined data illegally from Facebook users.

Why it matters: The embrace of machine learning models by political campaigns sharpens questions about how to maintain a functional democracy in the digital age. Machine learning enables candidates to present themselves with a different face depending on the voter’s likely preferences. Can a voter who’s inundated with individually targeted messages gain a clear view of a candidate’s positions or record?

We’re thinking: Modeling of individual preferences via machine learning can be a powerful mechanism for persuasion, and it’s ripe for abuses that would manipulate people into voting based on lies and distortions. We support strict transparency requirements when political campaigns use it.


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