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      Test systems carefully before deployment: Before an AI system is used in real-world situations,
                        it must be tested thoroughly. Testing helps identify unfair patterns, errors or weaknesses in the
                        system. Developers can then fix these issues before the system is widely used.

                        For example, if an AI system used for loan approval is tested with data from different income
                        groups and regions, developers can check whether the system treats all applicants fairly.
                       Allow human review of important decisions: Some decisions made by AI systems can have

                        a significant impact on people’s lives. In such cases, human review is important. Humans can
                        check the system’s decisions and correct them if necessary.
                        For example, if an AI system rejects a candidate during job screening, a human recruiter can
                        review the application again to ensure that no deserving candidate is unfairly rejected.

                       Monitor  performance regularly: AI systems  should  be  monitored  even after deployment.
                        Over time, new types of data and situations may arise. Continuous monitoring helps detect
                        new biases or errors and ensures that the system continues to perform fairly.

                        For example, a recommendation system used in online learning platforms should be monitored
                        regularly to ensure that it recommends content fairly to all students and does not favour only
                        certain groups.

                       Update models with new data: AI systems improve when they learn from new and updated
                        data. Regular updates help the system adapt to changing conditions and reduce bias.
                        For example,  a  navigation  system  should  be  updated  with  new road  information,  traffic
                        patterns and locations. This helps the system provide accurate and fair suggestions to users
                        from different areas.

                                A  titanium-alloy  smart ring  designed  for continuous  health
                                monitoring,  including  heart rate, blood  oxygen  levels and
                               sleep  quality.  It uses  AI to  analyse  biometric  data,  provide
                               personalised wellness insights and give early alerts for stress or
                       ai
                               fatigue.

                          lens

                     Importance of Fairness in AI

                     Fairness  is  an essential principle  in  Artificial  Intelligence  because  AI  systems  increasingly
                     influence decisions that affect people's lives. These decisions may relate to education, healthcare,
                     employment, finance and many other areas. If AI systems are not fair, they may create unequal

                     opportunities and negatively impact certain individuals or groups.
                     Some importance of fairness in AI system is as follows:

                       Ensures equal treatment for all individuals, regardless of gender, age, region or background
                       Reduces discrimination and prevents biased decision-making

                       Builds trust and confidence in AI systems among users

                                                                                          Ethics and Responsible AI    77

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