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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.
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 ...
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.
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 ...
Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias. [26] In natural language processing , problems can arise from the text corpus —the source material the algorithm uses to learn about the relationships between different words.
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 ...
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).
Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space , which allows an individual to calculate the relationships and similarities between ...