Page 101 - Computer Genius Class 08
P. 101

Bias in Real-World Data

                 Artificial Intelligence collects data and learn from
                 the  real-world.  Based on those  observations,  it
                 draws conclusion and plan accordingly for similar
                 situations  in  the future.  The problem  here, is
                 that the real-world, with all its irregularities and
                 confusion, is not a very ideal environment to take
                 inspiration  from.  The data collected by the AI

                 systems from the real-world is full of bias.
                 Real-world is full of bias, therefore, the data collected by the AI systems from the real-world is also
                 full of bias. For example, a computer system trained on the data for the last 200 years might observe
                 that more females were involved in specific jobs or that more percentage of successful businesses
                 were established by men. It might conclude that specific genders are better equipped for handling
                 certain jobs (gender bias).

                 Understanding  or  detecting  such  biases  is  not  an
                 easy  task  as  the  behaviours  of  AI  systems  is  not
                 simple  or  even  rational.  The  reason  behind  their
                 decision-making  is  not  easy,  and  in  some  cases,

                 just  impossible  to  understand.  Many  times,  the
                 programmers of AI systems themselves cannot explain
                 the logic behind decisions taken by the AI systems.
                 The Problem of Inclusion


                 The  consequence  of training  AI-systems on  biased
                 data is that they create the problem of inclusion, i.e.
                 the problem that some people will be left out of the AI
                 decision-making system. For example, the AI system
                 used by Amazon service for recruitment got affected
                 by gender-bias in its data. This created a situation in
                 which many eligible females were left out of consideration.

                 The Problem of Facts and Their Interpretation

                 All AI systems are  based on  facts  or  data  and  their
                 interpretations.  This  is the  root  of all  bias  problems
                 related to AI.

                 AI systems are trained to scan data and find learning
                 from it, but they are not e uipped to understand the
                 reason behind a particular conclusion or learning.






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