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  /  Artificial Intelligence   /  All You Need to Know About Bias in Artificial Intelligence
ARtificial intelligence AI bias

All You Need to Know About Bias in Artificial Intelligence

The issue is whether AI’s decisions will be less biased than human ones, or will it make issues worse?


A debate regarding biases and equality has been ignited by the use of artificial intelligence in sensitive fields, namely recruitment, criminal justice, and health. But in these and many other areas, human decision-making, which is mostly implicit, may also be faulty. The issue is whether AI’s decisions will be less biased than human ones, or will it make these issues worse?

Now some of the human bias embedded in the data they used during the tests has been replaced by the scientists who helped teach machines to see. The modifications will assist AI to see things more equally, they believe. But the effort shows that it seems difficult to eradicate prejudice from AI systems, partly because they still rely on human beings to train them.

What is bias in AI?

An inconsistency in the performance of machine learning algorithms is AI bias. These may be due to the prejudicial conclusions drawn in the training data during the period of algorithm creation or biases.

As per Towards Data Science report, “Amazon’s one of the largest tech giants in the world. And so, it’s no surprise that they’re heavy users of machine learning and artificial intelligence. In 2015, Amazon realized that their algorithm used for hiring employees was found to be biased against women. The reason for that was because the algorithm was based on the number of resumes submitted over the past ten years, and since most of the applicants were men, it was trained to favor men over women.”

AI Bias Forms

There are two main explanations for AI: cognitive bias and data bias.

Cognitive bias is an issue or defined AI disruption when an actual human distortion has been detected. Cognitive AI bias is thought of as an element of human prejudices, whether deliberately coded in or unintentionally conditioned to favor one data over the other. Face recognition systems that may have difficulty identifying darker complexions are examples of this kind of AI bias.

Data biases have to do with seed data that is used for machine learning instead of a concern with how things have been configured. If the leveraged information has been insufficient or obtained under some biases, bad data will lead to AI bias because the actual truth is not given by the data itself.

How can AI Bias be Minimized?

Understand the data on training

There can be divisions and categories on both academic and commercial datasets that lead to bias in the algorithms. The more you grasp your data and own it, the less probable it is that you will be shocked by offensive labels.

Be careful of technological limitations

Even best practices in design process and model construction, particularly in case of partial data, will not be sufficient to eliminate the chances of unwanted bias. The constraints of data, models, and technological solutions to bias are crucial to consider, both for the sake of knowledge and so that certain human strategies can be taken into account to limit bias in machine learning.

Research & Progress

These are important in order to minimize the bias in data sets and algorithms. Bias elimination is a multidisciplinary method consisting of social scientists, researchers and specialists who better understand the complexities in the phase of each application area. Businesses should also aim to include such specialists in their AI projects.