When algorithms shape the way

Since the dawn of time, humans have tried to understand themselves and their environment. The invention of computer technology has enabled this understanding to take place on a whole new level. Artificial intelligence is one result of this development that has been met with both excitement and concern by people all over the world. With AI’s ability to learn from data sets, computers are able to perpetuate human biases in ways never before possible.

This blog post will explore what algorithms can do when given biased input data sets, how machine learning works within the scope of artificial intelligence, and why it is so important for companies developing these technologies to be aware of their own bias which could lead them down a dangerous path

Imagine you want to create an AI system. You first start including images of self-driving cars in the data set of images , the systems train on and place more weight on accurately detecting those images. If there are not enough data, it is not representative of the population, for example there would be too few examples of dark skin people. And this is how algorithms work. They are everywhere, shaping the way we interact with people, objects and events. They decide what content to show on our social media feeds, which ads to display in our browsers and which job candidates get shortlisted for interviews. The problem is that algorithms also reproduce prejudices within society without us even noticing it!

It is not uncommon for AI to experience prejudices. The question is how can we eradicate them? One way of doing this would be by programming an algorithm with all the possible biases that could exist in society. This article explores the idea of algorithms reproducing prejudice and how they shape our world, especially when it comes to social justice.

Algorithms are shaping the way we think about life, and they’re doing it in ways that reproduce our prejudices. This is because algorithms are often created by people who share the same biases, or in this case, create them based on data that reinforces those biases. Data scientists at Google say that there’s a lack of diversity in their workforce which leads to an overrepresentation of white men when creating new products.