Biases in Artificial Intelligence and How to Reduce Them

Examples of bias in artificial intelligence relate to different social and moral-ethical aspects. One of the clear gaps in the use of advanced technologies is utilizing potentially racist algorithms for searching and processing information. For instance, some popular crime tracking software identifies black people more often than whites. Otherwise, special programs may not recognize African Americans in photographs or videos, which is directly associated with racism.

Confirmation bias is a dangerous trend that is also associated with under-advanced AI mechanisms. As an example, one can pay attention to the situation when search queries depend on the number of matching queries from people from the same network or community. For instance, a person who understands the benefits and importance of vaccination may see articles and digital materials about the dangers of vaccines more often if his or her friends on the social network adhere to this point of view. This type of bias is dangerous in that the user may be subjected to false facts and arguments based on others’ erroneous ideas, which, in turn, poses a real threat to well-being.

Gender bias, as a social gap, also occurs in AI algorithms. For instance, recruiting staff based on criteria embedded in special digital programs often raises the issues of infringement of women’s rights and makes vacancies for men more frequently viewed and promising. In a modern democratic society, such inequality is an unacceptable phenomenon. However, one of the main challenges is that AI is designed by real people, and the human factor can be part of the software. Therefore, initially, social prejudices among real people should be eliminated to make artificial intelligence more advanced and bias-free.

Given the existing bias in AI, individual measures can mitigate some gaps and help reduce moral and ethical tensions. As one of them, experts suggest paying attention to ensuring maximum transparency of software to control all the processes and avoid mistakes that are associated with socially unacceptable problems, for instance, racism. As a result, the involvement of legislators to resolve this topic is seen as a measure that may contribute to better debugging of artificial intelligence algorithms, thereby avoiding controversial issues. Companies using AI in their operational processes need to provide detailed reporting on how their digital applications work. This can allow minimizing cases of the incorrect operation of corresponding services due to improved technological solutions and maintenance approaches considered in detail and in advance.

An important aspect of ensuring the ethically correct operation of AI is to control the capabilities of networks using such technology. Experts point out that overpowering software is fraught with unforeseen consequences since artificial intelligence is guided by rational and maximally objective operating mechanisms, which may run counter to traditional moral values. At the same time, people themselves can also show consciousness and adapt to the use of AI in the context of global digitalization. Users of the global network should be aware of how such algorithms work in the marketing environment so that no advertising or other content could offend anyone’s feelings. Life in the AI era is a reality because many services use highly accurate mechanisms for tracking user activities, control over browsing, and other functions. Therefore, people need to take into account the capabilities of modern robotic systems and prevent the appearance of unwanted content through their own fault.

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