What is an AI Bias?

AI Bias means favoring someone or something. When Bias Data is fed to an AI Machine while creating the Model then the machine will also be biased.

AI Bias is an anomaly in the result produced through AI based programs and algorithms because of prejudiced assumptions made during the algorithm development process or prejudices in the training data.

For example :-
=> Most of virtual assistants have a female voice and not a male voice.
=> Security systems are trained, based on an individuals race of gender rather than their actions, movements to commit the crime ,etc.
=> In US, a healthcare algorithm was used to decide extra medical facilities for people, AI produced faulty results and favoured white patients over black patients.

Training data in AI

=> Data plays an important role in an AI model's functioning. AI models uses training data to learn and perform its task with high accuracy. Traing data may be biased during collection process that will produced biased results.

Training Data is a huge collection of labelled information that is used to build an AI model.

For example: Let us consider that a person Mr. X likes a color 'Red' very much. Now, if another dataset stores that 'Red' colour is preferred for aggressive nature, then without much representation, this dataset may link that person with aggressive nature. So this is an example of AI bias.

What is AI Access?

AI Access explain the gap in society, where only upper-class people who can afford AI-enabled devices have the opportunity to access it and people below the poverty line don’t have access to it.

Because of this, a gap has emerged between these two classes of people and it gets widened with the rapid advancement of technology.

The Government has to bring balance in the society by providing infrastructure to common students/people so that everyone will get a chance to access emerging technologies like AI.

AI access means making AI more accessible or available to all.