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Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms.
“If bias encoding cannot be avoided at the algorithm stage, its identification enables a range of stakeholders relevant to the AI health technology's use (developers, regulators, health policy ...
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.
The consequences of algorithmic bias could mean that Black and Hispanic individuals end up paying more for insurance and experience debt collection at higher rates, among other financial ...
Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes. [ 1 ] Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes.
Automation bias can be a crucial factor in the use of intelligent decision support systems for military command-and-control operations. One 2004 study found that automation bias effects have contributed to a number of fatal military decisions, including friendly-fire killings during the Iraq War. Researchers have sought to determine the proper ...
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 ...
Also known as current moment bias or present bias, and related to Dynamic inconsistency. A good example of this is a study showed that when making food choices for the coming week, 74% of participants chose fruit, whereas when the food choice was for the current day, 70% chose chocolate.