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Artificial intelligence (AI) is a rapidly developing technology which is already extensively utilized in different spheres, yet it has many considerable issues, and one of the most important of them is algorithmic bias. Any type of machine learning is based on exposing computers to large arrays of data and training them to perceive certain patterns, which then will be used for making decisions. Nevertheless, quite often, the data which computers process possesses many inherent biases which eventually can affect the decisions made by the AI. For instance, an AI system sorting hundreds of resumes in order to facilitate the screening process can favor male candidates over female ones if the majority of resumes it processed came from men (Sharma, 2019). Such an example demonstrates that the quality data can directly impact the accuracy and fairness of the actions of AI systems and cause them to encounter algorithmic bias capable of discriminating against people. Moreover, the data used by the machines is not the only problem which can promote bias since the role of the engineers is also significant, and their assumptions about data and results are crucial. The issues of algorithmic bias and the objectivity of engineers decisions are considerable because people interact with AI on a daily basis, and it frequently assists them in making certain decisions (Heilweil, 2020). As a result, if the conclusions of AI systems are biased, people who rely on them will end up promoting discrimination against minorities and individuals based on their gender, age, race, and other qualities. The effective solution for the problem of algorithmic bias would be to enforce strict control over the data processed by AI and used in machine learning in order to remove any possible inaccuracies.
References
Heilweil, R. (2020). Why algorithms can be racist and sexist. Vox. Web.
Sharma, Kriti. (2019). How to keep human bias out of AI | Kriti Sharma [Video]. YouTube. Web.
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