The Sustainable Development Goals represent 17 interconnected global goals to achieve a better future. By 2030, the UN is supposed to achieve the Sustainable Development Goals (SDGs).
So, in that case, how will AI help or impede these goals? This paragraph will examine AI’s potential advantages and disadvantages regarding environmental, societal, and economic outcomes.
AI and the environmental outcomes
Environmental applications of artificial intelligence (AI) are growing, including energy (e.g., smart grids), agriculture, and monitoring. As IoT hardware advances and AI algorithms for sensor fusion and vision evolve, this is becoming more and more possible.
Smart cities can improve efficiency and reliability by introducing and adopting innovations like Electronic Vehicles (EVs) and smart appliances. Furthermore, AI could be used to astutely manage renewable energy risks and bridge the gap between supply and demand through intelligent grids.
Artificial intelligence technologies can, however, be computationally expensive in some cases. In countries where dirty coal is still used to generate electricity, pollution can overshadow efficiency gains.
To grow sustainably, a green data center that is more efficient and based on renewable energy needs to be built, as well as embedding human knowledge into existing models through priors.
Unlike the current AI models, the human brain consumes much less energy (and works more efficiently, too). So, innovating on its integration (e.g., Deep learning based on physics) could benefit the environment and communities, particularly susceptible to excess AI-based pollution.
AI and societal outcomes
AI systems are still not accessible to low-income families or those from disadvantaged backgrounds because of their high costs. There will be an increased risk of rising inequality if the government fails to regulate how AI benefits are distributed among stakeholders.
There is a possibility that big producers would benefit. At the same time, smallholder farmers would be left behind since they cannot afford any expensive AI system that could boost their productivity and output.
Additionally, if enough transparency and diversity are not provided, racism, gender stereotypes, xenophobia, and hate crimes may increase. There has been evidence that most AI systems, especially those in Natural Language Processing and Computer Vision, still exhibit systematic bias and racism.
Since the data used for training have already been influenced by existing social bias, the device may adopt our subjective and irrelevant prejudices without being explicitly de-biased during data engineering.
Our models must be transparent and diverse to remain objective. A decentralization strategy could be used where AI technologies are implemented across teams from various cultures, ethnicities, racial backgrounds, and genders.
AI and economic outcomes
Artificial intelligence complements a person’s work, thus enabling them to produce more, be more productive, and achieve more significant results. With innovative agricultural technology, farmers will know when, where, and how to plant, while intelligent security algorithms will be able to monitor intruders and criminals.
However, suppose the current trajectory continues, and the future market heavily depends on data-driven economies. In that case, the income gap will most likely grow, particularly in low- and middle-income countries with limited resources for human capital development.
Due to automation and the creation of new jobs that favor those with graduate degrees, the average salary for high-school dropouts in the US has fallen 30% since the 1970s.
There is no doubt that the government in its role can play an essential role in minimizing the effects of creative destruction. However, some may argue that this segregation will naturally arise from the process.
Through its CareersFuture initiative and many other government-led initiatives, Singapore has been re-training and re-skilling its workforce to bridge the inequality gap.
For AI’s potential advantages to be equitable and sustainable, it is essential to identify risks. Our next generation of AI technologies will only be kinder and more sustainable if we acknowledge and address these shortcomings.