Page 41 - AI for Good - Impact Report
P. 41

AI for Good



                    Sustainable Development Goal 1: No Poverty




                   End poverty in all its forms everywhere
                   SDG 1 is facing a critical situation, with none of its 7 targets
                   on track as of 2024.  The UN warns that if current trends
                                    149
                   persist, an estimated 575 million people will still be living in
                   extreme poverty by 2030, greatly impacting their quality of
                                   150
                   life and well-being.  The UN further highlights that global
                   efforts to eradicate extreme poverty have suffered setbacks
                   due to the COVID-19 pandemic and other major shocks,
                   leading to the first increase in extreme poverty in decades
                   and reversing global progress by three years. 151
                   AI and SDG 1                                                                  Impact
                                                                          According to a study on the impact of AI on
                   AI solutions and technologies can have various impacts on SDG 1.   SDG 1 could act as an (positive) enabler for
                   First, the technology may indirectly contribute to SDG 1’s advance-  100% of the targets and act as an inhibitor
                                                                           (negative) for 86% of the targets. (Nature

                   ment by strengthening other SDGs such as SDG 9 or SDG 8.  152 153    Communications, 2020)
                   By enabling research and innovation, the benefits generated could
                   trickle down to SDG 1 via the creation of new products or services
                   that are more affordable or better suited to the needs of the most          Use case 1
                   vulnerable communities. For instance, using AI to reduce the costs   Using AI to make the process faster for
                   associated with agricultural practices (e.g., minimizing the use of   micro-finance loans and to provide access
                   fertilizers) could enable communities to enhance their quality of   to financial services to communities that
                                                                              traditionally have been underserved.
                   life. 154  This indirect influence represents the most significant posi-
                   tive impact of AI on SDG 1. Moreover, government support for AI in
                   innovation and economic growth could indirectly lead to improve-
                   ments in SDG 1.
                   Specific use cases linked to each target of SDG 1 can yield addi-
                   tional impact. For example, AI can enhance the efficiency of the
                   financial sector, thereby increasing accessibility for the 1.7 billion
                   adults lacking access to financial services. 155  However, the number
                   of AI use cases for SDG 1 is less than other SDGs, reducing the                  link
                   collaborative efforts between the technology and the Goal. For
                   instance, based on two UN reports, there are only 2 use cases out of        Use case 2
                   40 in the AI for Good: Innovate for Impact report, 156  and around 70   Improving climate forecasting to better
                   use cases out of 408 in the UN Activities on AI report. 157  prepare communities for extreme weather
                                                                         events and reduce exposure to climate risks
                   While AI can generate positive impacts for SDG 1, the potential risks   as aimed in target 5 of SDG 1.
                   associated with the technology for this Goal must be considered. AI
                   could widen inequality between countries, 158  and individuals. 159 160

                   Ownership of AI solutions could further create monopolies, leading
                   to a further concentration of wealth and power without equitable
                   compensation for the content providers. Additionally, investments
                   in AI and its infrastructures, such as robots in agriculture, can be
                   costly and may hinder access to technology for the poorest commu-
                   nities, further widening the wealth divide. 161  To assure fairness and          link
                   value for all, governments should account for new value-sharing
                   models in their legislation.                                                Use case 3
                                                                            Providing access to overlooked commu-
                   Key Considerations for Stakeholders                     nities to market solutions to provide them
                                                                             with a new revenue stream and higher
                   •  Technology access: A significant constraint in AI adoption is       financial resilience.
                      the cost of associated access to market, in terms of AI hard-
                      ware and software. For instance, not everyone can afford
                      robots to assist in their operations in agriculture. 162  Imple-
                      menting a sharing model or subsidizing hardware costs
                      could mitigate this risk.
                   •  Ownership sharing: To mitigate the risk of technology
                      monopolies, it is important to explore new business models
                      that distribute value differently, 163  aiming to minimize                    link
                      inequality growth and fairly reward all contributors.





                                                           31
   36   37   38   39   40   41   42   43   44   45   46