Difference between revisions of "Bias in Algorithms"
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| − | '''Bias in algorithms''' describe outcomes from computer systems “which are systematically less favorable to individuals within a particular group and where there is no relevant difference between groups that justifies such harms” | + | '''Bias in algorithms''' describe outcomes from computer systems “which are systematically less favorable to individuals within a particular group and where there is no relevant difference between groups that justifies such harms” <ref name="ref 4">.Lee, Nicol Turner, et al [https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/], Mar 2022.</ref>. Algorithms, “which are a set of step-by-step instructions that computers follow to perform a task,” are commonplace in the 21st century and can cause various biases <ref name="ref 4">. There are multiple causes of bias in algorithms that can be classified into the following four groups: data creation bias, data analysis bias, data evaluation bias, and the demographics of data scientists. As a result of these biases, algorithms can create or perpetuate racism, sexism, and classism. To prevent those biases from a technical standpoint, it is important to monitor and analyze algorithms during data creation, analysis, and evaluation. From a general standpoint, it is important to increase diversity and inclusion in companies to mitigate biases from every aspect. |
==Causes of Bias== | ==Causes of Bias== | ||
===Data Creation Bias=== | ===Data Creation Bias=== | ||
====Sampling Bias==== | ====Sampling Bias==== | ||
| + | ==References== | ||
| + | <references/> | ||
