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Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or ...
Similar biases have been uncovered in algorithms used to determine resource allocation, such as how much assistance people with disabilities receive. These are just a handful of many examples ...
Algorithm aversion is defined as a "biased assessment of an algorithm which manifests in negative behaviors and attitudes towards the algorithm compared to a human agent." [ 1 ] This phenomenon describes the tendency of humans to reject advice or recommendations from an algorithm in situations where they would accept the same advice if it came ...
The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. [1] Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g., step-functions in decision trees instead of ...
The algorithms learned the biased pattern from the historical data, and generated predictions where these types of candidates were most likely to succeed in getting the job. Therefore, the recruitment decisions made by the AI system turned out to be biased against female and minority candidates. [ 25 ]
Other research topics include the origins of bias, the types of bias, and methods to reduce bias. [4] In recent years tech companies have made tools and manuals on how to detect and reduce bias in machine learning. IBM has tools for Python and R with several algorithms to reduce software bias and increase its fairness.
A false-biased Monte Carlo algorithm is always correct when it returns false; a true-biased algorithm is always correct when it returns true. While this describes algorithms with one-sided errors, others might have no bias; these are said to have two-sided errors.
In marking down many individual students to prevent high grades increasing overall, the algorithm did exactly what the government wanted it to do. Here are the biased algorithms the UK government ...