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Specific to news media, an echo chamber is a metaphorical description of a situation in which beliefs are amplified or reinforced by communication and repetition inside a closed system. [ 36 ] [ 37 ] Based on the sociological concept of selective exposure theory , the term is a metaphor based on the acoustic echo chamber, where sounds ...
An echo chamber is "an environment where a person only encounters information or opinions that reflect and reinforce their own." [1]In news media and social media, an echo chamber is an environment or ecosystem in which participants encounter beliefs that amplify or reinforce their preexisting beliefs by communication and repetition inside a closed system and insulated from rebuttal.
Selective exposure has also been known and defined as "congeniality bias" or "confirmation bias" in various texts throughout the years. [1] According to the historical use of the term, people tend to select specific aspects of exposed information which they incorporate into their mindset.
GCF Global encourages online users to avoid echo chambers by interacting with different people and perspectives along with avoiding the temptation of confirmation bias. [58] [59] Yu-Ru and Wen-Ting's research looks into how liberals and conservatives conduct themselves on Twitter after three mass shooting events.
Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases. [166] Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information from its input signals ...
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model, it becomes more ...
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Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).