The relationship between generative AI and ESG is drawing ever greater attention as the technological progress of recent years dovetails with interest in the sustainability of technology. Now that generative AI—which sparks innovation across industries and offers opportunity and challenge at the same time—has settled into our daily lives, it is a good time to question whether we may enjoy AI technology without limit simply because it is convenient.
Carbon Emission vs. Carbon Reduction: the Two-Sidedness of AI Technology's Effects
[What Is the Relationship Between Generative AI and ESG? ⓒESG.ONL]
Viewed from the environmental side, one cannot ignore the criticism that generative AI is burdening the environment with its enormous power consumption. To train AI models, countless servers must be run at data centers. Because of the problem of increased carbon emissions in this process, the advancement of AI technology becomes entangled with environmental issues. Recently, a Chinese AI model called "DeepSeek" drew attention by touting low cost and high efficiency. Even so, data-center power use across the AI industry is surging, so it is not easy for the environmental problem to come off the chopping block. In particular, when electricity is drawn from regions highly dependent on fossil fuels, the carbon footprint inevitably grows larger.
On the other hand, news is also heard at the same time that generative AI is contributing to establishing companies' carbon-reduction strategies by analyzing greenhouse-gas emission sources. AI can also contribute to the planning and design of sustainable products. Therefore, it is not right to lean only toward the negative thought that generative AI simply harms the environment.
Privacy Controversy and the Possibility of Solving Social Problems Coexist
On the social side, data-privacy and ethical problems remain. An AI model like DeepSeek—which drew hot attention on a global scale—became embroiled in controversy over suspicions that it stores user data on Chinese servers, and in Korea there was even an incident in which access to DeepSeek was blocked. Such cases are also problems directly linked to consumer trust in AI technology and social responsibility. In addition, AI's learning of erroneous data can produce biased results such as racial or gender discrimination, and advanced deepfake technology carries the risk of leading to crime. Because social responsibility at the ESG level is also connected to protecting personal information and fair use of technology, there is a need to pay attention to such problems of AI technology.
There are also cases opposite to the worrisome ones. Projects like Microsoft's "AI for Good" continuously show AI's positive social impact by using AI to contribute to disease diagnosis, the improvement of public health, and the advancement of human rights.
Contributing to Strengthening Information Transparency... Unclear Accountability and Data-Security Problems Are Homework
Viewed from the governance side, AI can contribute to raising corporate transparency in ways such as monitoring regulatory compliance in real time. Of course, the risk of posing risks to governance due to data-security problems coexists. To prevent negative outcomes, global companies have begun to introduce new standards for digital ESG and seek transparent modes of operation suited to the AI era.
In this way, the topic of generative AI and ESG has become an inseparable relationship. From the environmental side, there is the two-sidedness of the burden of carbon emissions and improved efficiency; socially, it carries both privacy controversy and the possibility of solving social problems at the same time; and in governance, there is the possibility of it acting as a tool that strengthens ethics and transparency. Considering the ESG-level impacts during AI development and use is not merely jumping on a trend but an essential task to consider. We must watch how the producers and consumers of AI technology strike this balance going forward.
by Editor N