Reader TTS

Visualizations have an imperative for conveying exact information to an audience it does not know. It carries the critical part of story telling where the story needs to be precise and

exact. Although the interpretation of the visualization depends on the audience. Lets talk about what all things can affect a reader if different kind of biases are involved in creating of a

visualization. Broadly we will talk about three types of bias: * Author Bias * Data Bias * Reader Bias AUTHOR BIAS Lets 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 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 Data bias can be introduced in multiple ways, be it faulty surveying methods or biased sampling of

data or may be through any other data collection or processing technique. The best way to correct data bias is to have a very clear and unbiased process of data gathering. 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 Reader bias comes from a reader

from a certain 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 bias does not affect the outcome of the story. The narrative as a whole should be

neutral and convey the facts and inferences based on data. Accounting for the different kind of biases aids the narrative and avoids incorrect interpretations.