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VOL.1 , ISSUE 71OL.1 , ISSUE 71
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                  significantly higher error rates for people           such  as  AI  for  precision  agriculture  in

             of  color  and  sometimes  up  to  30%  more       Kenya  to  combat  food  insecurity,  or  AI-
             leading  to  misidentification  and  wrongful      powered     healthcare     diagnostics    for
             arrests  in  countries  like  the  US,  or         prevalent  diseases  in  Nigeria.  These
             ineffective    deployment       in    diverse      initiatives  aim  to  build  indigenous  AI
             populations  globally.  This  demonstrates         capabilities  reducing  reliance  on  external
             how  AI  when  not  informed  by  diverse          technologies      and      fostering     self-
             cultural  and  social  contexts  can  perpetuate   determination.  Finally,  it  envisions  a
             and even amplify existing societal biases and      decolonized  AI  future  as  a  catalyst  for
             injustices.                                        global  justice  and  empowerment.  As  to
                                                                imagine  if  AI  systems  were  designed
                                                                collaboratively       with        indigenous
                                                                communities     to    preserve   endangered
                                                                languages  or  AI  tools  developed  by  local
                                                                farmers in Southeast Asia to optimize crop

                                                                yields  and  manage  water  resources  directly
                                                                addressing  climate  change  impacts.  This
                                                                requires  a  fundamental  shift  from  a  top-
                     Thirdly,  the  article  outlines  a  multi-  down  extractive  model  to  one  based  on
             faceted    approach     to   achieving     AI      cocreation,  equitable  data  governance,  and
             decolonization  emphasizing  policy  reform,       shared  benefits.  For  instance,  initiatives
             localized  innovation,  and  shifts  in  global    promoting  open-source  AI  models  and
             governance.  According  to  the  European          federated  learning  could  allow  data  to
             Union's     General      Data      Protection      remain  localized  while  still  contributing  to
             Regulation  (GDPR)  while  not  explicitly         global  AI  advancements  and  by  actively
             decolonial  offers  a  model  for  robust  data    dismantling the colonial legacies embedded
             protection  and  individual  rights  that  other   within current AI paradigms, we can ensure
             regions  are  now  adapting  such  as  Brazil's    that  the  transformative  power  of  AI
             LGPD or India's proposed data protection           genuinely serves the collective well-being of
             bill aiming to assert greater national control     all  nations  fostering  a  more  inclusive  and
             over data. Crucially, localized innovation is      equitable  technological  landscape  for  the
             emerging  in  places  like  Nairobi's  “Silicon    21st century.

             Savannah” or Lagos's Yaba where startups
             and  research  hubs  are  developing  AI
             solutions tailored to local challenges


















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