Over the last decade, predictive statistical models have emerged that can uncover private traits about individuals without their consent. These traits, such as personality or mood, are predicted through various machine learning methods, using digital records of online activity such as social media data. Predictive models have allegedly been used by “propaganda machines” that target individuals with ideas or advertising. The use of predicted private traits has been shown to be an effective means of mass persuasion that can significantly increase product sales. Now we are seeing firms like Cambridge Analytica and Aggregate IQ employing these tools for political causes like Brexit and candidates such as Donald Trump. Psychological profiling using social media data was reportedly used for voter suppression — discouraging people from casting their ballots — in the 2016 US presidential election. Cambridge Analytica claimed it used 5,000 data points per adult voter in the United States to create targeted ads for the Trump campaign.
In Canada, it is unclear how this technology is in use. In 2015 the Liberal Party of Canada hired Sean Wiltshire, a microbiologist, to overhaul Liberalist, its in-house analytics platform. The overhaul was modelled after analytics tools used in the 2008 and 2012 Obama campaigns, which predicted the behaviour of individual voters with unprecedented accuracy.
A 2018 study involving 3.5 million individuals revealed that using predicted private traits to design advertising increased the number of purchases from such advertisements by 50 percent compared with control advertisements. If the use of these techniques in political advertising can influence voters’ choices to the same degree, they could pose a risk to fair elections and the democratic process.
Full Article: Block the parties from predicting voters’ private traits.