Difference between revisions of "Bias in Algorithms"
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| − | == Bias == | + | '''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” [4]. 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 [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== | ||
| + | ===Data Creation Bias=== | ||
| + | ====Sampling Bias==== | ||
Revision as of 16:09, 26 January 2023
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” [4]. 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 [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.
