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Cognitive Biases

What are Machine Biases?

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Machine biases are patterns of mistakes in the logic of software such as algorithms or artificial intelligence. These are analogous to cognitive biases in people. Where human biases are well understood, the study of machine biases is in its infancy. Machines make mistakes in logic for several reasons:


Design errors, bugs or biases that are intentionally incorporated into software.

Learning Algorithms

Machine learning algorithms that cause an artificial intelligence to develop flawed models.

Ensemble Learning

Ensemble learning is a technique that combines results from multiple learning algorithms. It is similar to social processes and may contain biases that are analogous to social biases such as the abilene paradox.

Training Data

Artificial intelligence that is trained using data that gives it the wrong impression about the world. For example, an AI that watches too many old Cowboy movies might assume that gun fights often break out during poker games.

Outliers & Black Swans

The statistical models used by artificial intelligence may have difficultly processing outlier exceptions or small changes in certain parameters that result in major events. Humans may be better equipped to deal with surprising situations using skills such as creativity and synthesis.
Overview: Machine Biases
Patterns of logical errors in software such as artificial intelligence and algorithms.
Related Concepts

Cognitive Biases

This is the complete list of articles we have written about cognitive biases.
Ambiguity Effect
Backfire Effect
Base Rate
Circular Reasoning
Cognitive Bias
Cognitive Dissonance
Complexity Bias
Crab Mentality
Creeping Normality
Curse Of Knowledge
Decoy Effect
Exposure Effect
False Analogy
False Hope
Fear Of Youth
Gambler's Fallacy
Golden Hammer
Halo Effect
Hindsight Bias
Negativity Bias
Optimism Bias
Peak-End Rule
Positive Bias
Sour Grapes
Survivorship Bias
Us vs Them
Victim Mentality
Wishful Thinking
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