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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
                 collected fairly, key information is missing,              Accountable       Safe, Secure,
                                                                                                  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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