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However, the largest data collected from the real world may also reflect human subjectivity and underlying social
              biases. The Amazon AI recruitment system is a good example. It was found that their recruitment system was
              not selecting candidates in a gender neutral way.  The machine learning algorithm was based on the number
              of resumes submitted over a period of 10 years, that most of them were men, so it favoured men over women.
               • Algorithms: The algorithms in itself do not add biasness to an AI model, but it amplifies the biasness. Let’s
              see an example of an image classifier model. Its trained on images in the public domain—pictures of people's
              kitchens. It so happens that most of the images are of women rather than men. AI algorithms are designed to
              maximise the accuracy. Therefore, an AI algorithm may decide that the people in the kitchen are women despite
              the fact some of the images are of men.
               • Developers: The last source of AI bias is developers. Those who design AI models focus on achieving the
              desired goals. On that path at times, the biases of the developers are reflected in their models. It's important to
              note here that the ethics and AI bias are not the problems of the machine but the humans behind the machines.


                    AI Access


            AI Access can be acquired by two means:
               • Data availability: AI needs access to huge data sets so that it can analyse it and draw conclusions and learn
              from it.
               • Abilities: AI needs access to capable hardware to turn its learning into useful action. Self-driven cars are
              examples of AI with right access.

            AI is used on bigger, faster and more expensive machines. AI is a privilege which only a few people can afford
            and take advantage of this new technology. This has created a gap between these two classes of people and it
            gets widened with the rapid advancement of technology.


                    Advantages of AI

            The following are the advantages of AI:
               • Increased Automation: AI can be used to automate anything, ranging from tedious tasks to the process of
              recruitment of employees. There are AI-based applications that can be used for the hiring of new recruits. Mya,
              is an AI app that automates tedious parts of the recruitment process such as sourcing, screening and scheduling.

               • Increased Productivity: AI is widely used in businesses. It is used to manage highly computational tasks that
              require maximum effort and time. Starting from the staff recruitment to automated packing of products, AI has
              the potential to increase productivity.

               • Smart Decision Making: AI helps in making smart decisions. Salesforce Einstein Analytics is a comprehensive
              AI for CRM (Customer Relationship Management), designed to enable companies to make smarter business
              decisions. The goal of this technology is to give sales and marketing departments more comprehensive customer
              behaviour and sales prospects.
               • Solve Complex Problems: AI is being used in solving complex problems that cannot be solved by human
              beings. Through the years AI has progressed from simple machine learning algorithms to advanced machine
              learning concepts that have helped resolve complex problems like fraud detection, medical diagnosis, weather
              forecasting and so on.
               • Strengthens Economy: AI is estimated to contribute $15 trillion to the world’s economy by 2030. It is estimated
              that the greatest impact of AI will be in the field of healthcare and robotics. The rise in the economy will mainly
              be from substitution of work by automation and increased innovation in products and services.
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