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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 ...
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).
A low-probability observation string is one that can only be generated by a long computer program. Algorithmic probability is closely related to the concept of Kolmogorov complexity. Kolmogorov's introduction of complexity was motivated by information theory and problems in randomness, while Solomonoff introduced algorithmic complexity for a ...
24/7 Help. For premium support please call: 800-290-4726 more ways to reach us. Sign in. Mail. ... we had widespread evidence of bias in AI algorithms, which are often less accurate for some ...
Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes. This means they ought to evaluate only relevant characteristics of the input data, avoiding distinctions based on attributes that are generally inappropriate in social contexts, such as an individual's ethnicity in legal judgments.
The bias–variance decomposition forms the conceptual basis for regression regularization methods such as LASSO and ridge regression. Regularization methods introduce bias into the regression solution that can reduce variance considerably relative to the ordinary least squares (OLS) solution. Although the OLS solution provides non-biased ...
Internally, the algorithm consults two tables, a probability table U i and an alias table K i (for 1 ≤ i ≤ n). To generate a random outcome, a fair die is rolled to determine an index i into the two tables. A biased coin is then flipped, choosing a result of i with probability U i, or K i otherwise (probability 1 − U i). [4]
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 ...