Page 69 - Ai V2.0 Flipbook C8
P. 69

6.   As AI gets smarter, how can people keep important jobs? What special things about humans

                     should we keep working on?



                  7.   How can we keep people’s personal information safe when AI collects and looks at lots of
                     data?




                  8.   What  can  be  done  when  building  AI  to  stop  it  from  being  unfair  or  biased?  Why  is  it
                     important to have different kinds of people help make AI?








                               Task
                                                                                              21 st  Century   #Communication
                                                                                                  Skills
                   Set up a mock parliament session where the class will be divided into two groups representing
                   ruling party and opposition. The Ministry of Science and Technology will lead the session
                   analysing the ethical issues of implementation of AI in our country. The two sides will debate
                   for and against and the voting will take place at the end.






                          At a Glance


                       • Ethics are the moral behaviour of humans in given circumstances in their social life.
                       • Artificial Intelligence (AI) ethics can be defined as a set of values, principles, and techniques
                      that guide moral conduct regarding right and wrong in the development and deployment of AI
                      technologies.
                       • Different ethical issues using AI are bias and discrimination, privacy violations, lack of transparency,
                      job  displacement,  security  risks,  autonomous  weapons,  accountability,  unequal  access,  and
                      ethical decision-making in AI systems.
                       • AI bias means the AI might make wrong or unfair decisions, even if it wasn’t meant to.
                       • Some common types of AI bias are Data bias, Algorithm bias, Selection bias, Confirmation bias,
                      Measurement bias, Exclusion bias, Group Attribution bias, Historical bias, and Societal bias.
                       • To make AI systems more accurate, fair, and trustworthy, it is essential to recognise and address
                      the factors that cause these biases.








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