In companies and businesses, artificial intelligence (AI) has been seen as a transformative strategic resource. It has aroused great interest due to its potential for various sectors, through the automation of tasks, personalization of services and increased analytical and predictive capabilities.
However, AI systems also pose risks to people and society. They can involve discriminatory biases in data, threats to privacy and human dignity. While the field of AI Ethics considers principles and values underlying such systems, attention has only recently been drawn to the material (Crawford, 2021) and physical (Floridi, 2024) dimensions of AI. This signals impacts on the natural environment – energy, water and mineral consumption – and on the social dimension – working conditions in the sectors that support AI, disruptive changes in education, and the way we live (Crawford, 2021).
Looking at the artificial intelligence industry, i.e. the multiple industrial segments that provide it with supplies and services, can reveal the real impacts caused in the social and environmental spheres. One example is the microconductor production chain and its geopolitical importance, as explained by Chris Miller(see the article on Miller’s book). However, the AI industry and its impacts seem little accessible to public scrutiny. A better understanding of such effects for future generations are important aspects in the development of sustainable business models. These are important aspects for the decisions of companies and governments that are considering how to make effective and sustainable use of AI.
Sustainability of AI
Although artificial intelligence has shown positive advances for sustainability, little is known about the “Sustainability of AI”, i.e. its impacts on the environment and society (van Wynsberghe, 2022). Furthermore, “Green AI” and “AI Sustainability” deal more with environmental impacts, but tend to have a limited scope on human, social, political and institutional impacts.
The sustainability of AI should not be understood as a mere technical or compliance issue, but should involve an ethical and sustainable application of AI, weighing up the impacts of its production chain. This involves recognizing the stakeholders involved and the company’s role as part of a political community (in the Aristotelian sense). Such ethics – shared and dynamic – should contain a vision of how to live sustainably in an environment in which technology is used appropriately, respecting the place we inhabit and have bequeathed from past generations ( tradition, in MacIntyre’s terms) as well as acting so that future generations can enjoy similar/appropriate conditions.
In the local context, companies have a crucial role to play around these issues. It’s not just about managing risks and complying with a checklist of corporate metrics for sustainability, but about long-term consistency in monitoring and making a positive contribution to sustainability.
AI governance
Knowing the positive and negative impacts of AI is important to prepare organizations for responsive AI governance. The massive growth in its application requires companies to take a decisive and responsible role in the impacts of AI on the planet, people and the economy.
AI can contribute to controlling the carbon footprint and energy consumption, but it also emits carbon and consumes a lot of energy, especially during its training and to maintain its operational infrastructure (such as data centers). Socially, it can contribute to the Sustainable Development Goals (SDGs) (Di Vaio et al., 2020), but it can pose a risk to people and society, threatening privacy and living and working conditions.
The same goes for governance: it can speed up decision-making, but it can also represent centralization of power, manipulation of information and excessive surveillance. AI can therefore be a double-edged sword in terms of sustainability, which requires prudent action on the part of economic agents.
References
Crawford, K. (2021). Atlas of AI: power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press.
Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283-314. https://doi.org/10.1016/j.jbusres.2020.08.019
Floridi, L. (2024). The hardware turn in the digital discourse: An analysis, explanation, and potential risk. Philosophy & Technology, 37-39. https://doi.org/10.1007/s13347-024-00723-1
van Wynsberghe, K. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics,1, 213-218. https://doi.org/10.1007/s43681-021-00043-6