Google Initiates Development To Eliminate Traces Of Biasness Present In Its Algorithm

Google established itself as a tech giant primarily on the basis of its search engine and over the years, developments to the search engine are extended by Machine Learning and Augmented Reality programming. As more and more of the digital age is shifting its focus towards globalization by the means of social media and the internet, the role and statistical data of machine learning keep on expanding. The extension has entered a state such that it has started to group individuals into categories based on the extensive data it has and is limiting the potential of the users to just the things that they would prefer and doesn’t suggest new things to open to, creating a bias in the machine’s system.

This has lead many researchers and technological sociologists to believe that the bias adopted by the technology exists in the structures of the human brain and its actions on the internet. However, it was also suggested that this could be corrected by developments in the machine learning program. Google has taken over the suggestion and actions to implement an unbiased machine learning algorithm over all of its structures and acquisitions.

The first update revolved around providing researchers and users with the feature to Know Your Data. This is a feature for researchers that highlights to them if there exists an existing bias in their laid out data search parameters. The feature utilizes the image capture mechanism to examine their database and figure out the bias. This works as a first step leading to greater steps in the future as by eliminating subconscious bias, the researcher’s data can be more accurate and real to the cause.

However, the ability of the Know Your Data feature is still limited as it is still under development and not a finished Google Product. But the developments in to the feature are amazing and it can be expected that before we completely step into the Digital Age, most probably dominated by Google, all our transactions and searches will be empty of bias and problems.

Even though there is still more room to explore in the area of algorithm analysis and machine learning structures, it can be concluded that the steps are in the right direction, where it can be formatted to present an algorithm and database to ensure equal and indiscriminate opportunities to individuals, in a digital world free of racial prejudice, gender discrimination and class boundaries for success.

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