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Normalcy bias, or normality bias, is a cognitive bias which leads people to disbelieve or minimize threat warnings. [1] Consequently, individuals underestimate the likelihood of a disaster, when it might affect them, and its potential adverse effects. [ 2 ]
In psychology and cognitive science, a memory bias is a cognitive bias that either enhances or impairs the recall of a memory (either the chances that the memory will be recalled at all, or the amount of time it takes for it to be recalled, or both), or that alters the content of a reported memory. There are many types of memory bias, including:
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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).
The neglect of probability, a type of cognitive bias, is the tendency to disregard probability when making a decision under uncertainty and is one simple way in which people regularly violate the normative rules for decision making. Small risks are typically either neglected entirely or hugely overrated.
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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 ...
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