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The introduction of Artificial Intelligence has improved operations in almost every sector, industry, and part of human life. The use of AI has been vital in the department of justice, recruitment by organizations, facial recognition by police, and school admissions. The aim of introducing AI algorithms in various fields was to reduce human bias in decision-making. Despite the progress, there are ethical concerns that the AI algorithms also exhibit biases. The main reason behind the claim is because human developers are in charge of training data used by the algorithms. There are areas where the issue of biases affects human life directly and can do damages to a person, physically or emotionally. Some examples are college admissions, recruitment, administration of justice at the courts, public benefits systems, police, public safety, and healthcare. There are high chances that the development process introduced biases in artificial intelligence algorithms, knowingly or unknowingly, during any area mentioned above. The paper provides background knowledge on AI bias and possible solutions to solve the problem

Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is crucial to remember that the developers are human and might pass the biasness to the algorithms during training. The main target of this paper is to give background knowledge on opinion extraction and sentimental analysis and how factors affecting mental health can be collected. The paper aimed to use interested individuals in knowing some of the algorithms in opinions extraction and sentimental analysis. The paper also provides benefits of using sentiment analysis and some of the challenges of using the algorithms.

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