Visualizations have an imperative for conveying exact information to an audience it does not know. It carries the critical part of storytelling, where the story needs to be precise and exact. Although the interpretation of the visualization depends on the audience. Let’s talk about how all things can affect a reader if different biases are involved in creating a visualization.
Broadly we will talk about three types of bias:
- Author Bias
- Data Bias
- Reader Bias
Let’s say the ‘Author’ creator of the visualization wants to present the findings in a certain way, which would aid his/her ‘Story.’ The moment this action of telling a story in a certain way is taken, the author has introduced a bias into the narrative. Querying a visualization factually and logically can help catch and correct author bias.
Data bias can be introduced in multiple ways: faulty surveying methods or biased sampling of data, or maybe through any other data collection or processing technique. The best way to correct data bias is to have a clear and unbiased data gathering process. Thoroughly understanding and auditing the data collection and processing can help put a cap on data bias. Identifying a data bias is a very important task.
Reader bias comes from a reader from a specific domain who has preconceived notions about something in a particular domain. These readers, while interpreting a visualization, can introduce a reader bias. An ideal visualization considers reader bias and accounts for it while conveying its narrative. So that the reader’s bias does not affect the story’s outcome.
The narrative should be neutral and convey the facts and inferences based on data. Accounting for the different biases aids the narrative and avoids incorrect interpretations.
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