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Transparent, Explainable

                  People should know the purpose of data collection and agree to it. This helps others trust the
                  findings and check them if needed. For example, if you are collecting survey answers, people
                  should know the purpose and agree to it.


                  Fair and Unbiased

                  Using data from only certain people or places can create unfair or one-sided results. Data
                  should not be used to harm or discriminate. For example, the automation in the process of job

                  selection should not reject applications based on the discrimination against certain races. It
                  should help everyone, not hurt a specific group.


                  Privacy and Data Protection
                  Data must be protected, and individuals' privacy should be respected. It must not be shared
                  without their approval. For example, data collection should not reveal anyone's identity when
                  surveying a mall's busiest hours.


                  Accountable
                  Accountability means taking responsibility for your actions, especially when things go wrong.
                  In statistical data, data collection plays a key role. The person who collects the data, the data
                  analyst,  and  the  decision  maker  are  all  equally  responsible  if  something  goes  wrong.  For
                  example, if the government uses test score data to rank schools, then it should be clear who
                  would be accountable if any school is ranked low due to incomplete or biased data.


                  Safe, Secure, and Sustainable                     Transparent,        Fair and         Privacy and
                                                                     Explainable        Unbiased       Data Protection
                  Statistical data related to safety, such as
                  crime rates, road accidents, or workplace
                  issues,  can  be  biased  if  the  data  is  not
                                                                                               Safe, Secure,
                  collected fairly, key information is missing,
                                                                             Accountable           and
                  or it is incorrectly interpreted.                                             Sustainable


                                                                                          21 st  Century   #Media Literacy
                                                                                             Skills
                            Video Session

                       Watch the video of "Ethics of AI: Challenges and Governance" at the given link:
                       https://youtu.be/VqFqWIqOB1g?si=WNFTIqSTjeKanaaT OR scan the QR code and
                       answer the following question:
                       Can you point out other ethical issues related to AI which are not discussed
                       in the chapter?







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