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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.
These manipulations often stem from biases in the data, the design of the algorithm, or the underlying goals of the organization deploying them. One major cause of algorithmic bias is that algorithms learn from historical data, which may perpetuate existing inequities. In many cases, algorithms exhibit reduced accuracy when applied to ...
One of the most notorious examples of representational harm was committed by Google in 2015 when an algorithm in Google Photos classified Black people as gorillas. [9] Developers at Google said that the problem was caused because there were not enough faces of Black people in the training dataset for the algorithm to learn the difference ...
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).
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 ...
Algorithmic biases framework refers to the systematic and unjust biases against certain groups or outcomes in the algorithmic decision making process. Häußler says that users focus on how algorithms can produce discriminatory outcomes specifically when it comes to race, gender, age, and other characteristics, and can reinforce ideas of social ...
For example, when getting to know others, people tend to ask leading questions which seem biased towards confirming their assumptions about the person. However, this kind of confirmation bias has also been argued to be an example of social skill; a way to establish a connection with the other person. [9]