All news data contains inaccuracies. Whether through limited sample sizes, time lags or outdated sources, the majority of data presented to us contains a certain margin of error. Currently hidden from our perception, these inaccuracies can lead to an incomplete or simplified view on complex subjects. Using one of the first practical applications of quantum computing, this project will visualise this inaccuracy. By providing this extra layer of information, the aim is to help readers make more informed conclusions on the news they consume.
On a broad level, its tackling the challenge of inaccurate or misleading information in the news. One solution is to give readers context around data they see in the news. If they understand a given polling statistic has a margin of error, for example, that information can help contextualise the news item. This solution will make use of the probability-driven world of quantum computing to model inaccuracies in news data. The result will be a simple, universal, visual tool which can show the margin of error in a certain statistic.
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