189 Lutiana Valadares Fernandes Barbosa and Juliana Moreira Mendonça shared, “the value of information no longer resides solely in its primary purpose” (Cukier, K. & Mayer-Schoenberger, 2013). b. Carbon footprint AI development and use requires vast amounts of energy, for instance, it depends on myriad specialized computer chips, other computational resources, and materials (Luccioni et al., 2023) which is likely to elevate the world’s carbon emissions, especially if data centers get their energy from fossil fuels. If they use renewable resources, the impact decreases but still exists (Erdenesanaa, 2023) “In a middle-ground scenario, by 2027 AI servers could use between 85 to 134 terawatt hours (Twh) annually. That’s similar to what Argentina, the Netherlands and Sweden each use in a year, and is about 0.5 percent of the world’s current electricity use.” (Erdenesanaa, 2023). Research has shown that the Global North was responsible for 92% of excess global CO2 emissions (Hickel, 2020) whereas the impacts of climate change are most severely felt at the Global South By perpetuating high-carbon-emitting behaviors, AI systems play a significant role in exacerbating the climate crisis. Furthermore, the chatbots and image generators popularity is expanding, which strengthens tech titans’ competition and investment in the field. This scenario is likely to expand the market and further deepen environmental concerns (Keller, 2024). The environmental impact of AI, combined with its financial costs means a double punishment for traditionally marginalized communities. Not only are they less likely to enjoy the progress of technology but also, they are more likely to suffer the environmental impacts of its resource demands (Bender at al., 2021, p. 610). Considering AI´s negative environmental impacts, experts are attempting to make AI greener, which requires, among other things, transparency so that stakeholders can know how much electricity computers are using and how that translates into carbon emissions and other environmental indicators. The metrics and measurement tools of AI carbon footprints need to go through a standardization process to enable stakeholders to compare the impacts of various systems. Incentives are also crucial to encour-
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