The contribution of AI distortions: What does bias mean? First appeared at the online magazine Basic Thinking. You can start the day well every morning via our newsletter update.
Artificial intelligence should be objective, but often unconscious distortions sneak into AI models. This phenomenon is also called bias and caused by distorted or unbalanced training data.
Humans are biased by nature, because experiences, upbringing or culture can influence our thinking and decisions. These cognitive distortions can occur either consciously or unconsciously.
The situation is similar with artificial intelligence, in which bias is referred to as bias. But how does this phenomenon come about and what does Bias pretend?
KI: What does bias mean?
AI systems are trained with large quantities of data. But if these training data are unbalanced, bias can skip on artificial intelligence. A so -called bias is created, which is also referred to as systematic distortions in the results or decisions of an algorithm.
For example, this can lead to an algorithm preferred certain groups and disadvantaged others. Discriminating decisions, which mostly also reflect or even reinforce existing social inequalities, can result.
The main problem is missing transparency. Because while people can reflect on their bias and, if necessary, change, the Bias of AI often remains hidden. This makes the correction difficult when bias has first crept into a AI system.
For this reason, it is necessary to carefully select training data for AI systems. Algorithms must also be checked regularly to minimize the risk of bias.
What are the causes of AI distortion?
But not only biased training data can lead to a bias in AI systems. Incorrect or incomplete data records can also be responsible for distortions. Because these can lead to incorrect conclusions.
The way in which algorithms are programmed and optimized can also be given or disadvantaged in unintentionally. This can be triggered, for example, by a lack of diversity in the development team. If one-sided perspectives in the development and evaluation of AI systems are already available, unconscious prejudices can remain undetected.
If only one of these causes occurs in the development or training of AI systems, this can have serious consequences. Because AI models are trained to recognize patterns. Existing patterns in data records are then automatically reinforced. If the specified patterns already contain social inequalities, this leads to further tightening by using AI systems.
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The contribution of AI distortions: What does bias mean? First appeared on Basic Thinking. Follow us too Google News and Flipboard Or subscribe to our update newsletter.
As a Tech Industry expert, I understand that bias in AI refers to the unfair prejudice or favoritism towards certain groups or individuals that can be embedded in the algorithms and data used to develop AI systems. This bias can lead to discriminatory outcomes, perpetuate stereotypes, and exacerbate existing inequalities in society.
AI distortions caused by bias can have serious consequences, such as reinforcing gender or racial disparities in hiring decisions, criminal justice sentencing, or access to financial services. It is crucial for tech companies to address bias in AI systems by implementing ethical guidelines, diverse representation in development teams, and rigorous testing and monitoring processes.
Overall, as the Tech Industry continues to advance in AI technology, it is imperative that we prioritize fairness, transparency, and accountability to ensure that AI systems do not perpetuate harmful biases and distortions in society.
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