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    BIAS AND FAIRNESS IN AI

                      Artificial Intelligence systems learn from data. The quality and nature of this data play a very
                      important role in how AI systems behave. If the data used for training is incomplete, unbalanced
                      or reflects existing inequalities, the AI system may also learn these unfair patterns. This problem
                      is known as bias in AI. Bias occurs when an AI system produces results that are unfair, unequal
                      or prejudiced toward certain individuals or groups.

                      Bias in AI is often unintentional. It usually happens because the data used to train the system
                      does not represent all groups equally. Since AI systems rely on patterns found in data, they may
                      repeat or even strengthen existing social biases. This can lead to unfair outcomes that may affect
                      people's opportunities, safety and daily lives.

                      Let us consider a simple example. Suppose a company uses an AI system to shortlist candidates
                      for a job. The system is trained using past hiring data. If, in the past, most employees hired were
                      from a particular background or gender, the AI system may learn to favour similar candidates. As
                      a result, equally qualified candidates from other backgrounds may be rejected unfairly.

                      Another example can be seen in facial recognition systems. If a system is trained mostly with
                      images of people from one region, it may not correctly recognise people from other regions. This
                      happens because the system has not learned from diverse data. As a result, the system performs

                      well for some groups but poorly for others.

                      Effects of Bias in AI

                      An unfair or biased AI system may create several problems, such as:
                        Reject deserving candidates during hiring or college admissions

                        Spread stereotypes about certain groups of people
                        Ignore minority groups or underrepresented communities

                        Make incorrect predictions or decisions
                        Provide unequal services to different users

                      Such outcomes can negatively affect individuals and may also reduce trust in AI systems.

                      Reducing Bias in AI Systems

                      Developers and organisations take several important steps to reduce bias and ensure fairness in
                      AI systems, which are as follows:

                        Use diverse and representative data: Training AI systems with data from different regions,
                         genders, age groups, languages and backgrounds helps create balanced models. When the
                         data represents a wide variety of people and situations, the system learns to make fair and
                         accurate decisions.

                         For example, if a speech recognition system is trained using voices of people from different age
                         groups, accents and languages, it will better understand a wider range of users. Otherwise, it
                         may only work well for a limited group.

                       76        Artificial Intelligence (CT & AI)-VIII

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