Small Data for sustainability: AI ethics and the environment

Moving away from the currently prevalent Big Data mindset towards a Small Data approach would help improve the sustainability of AI systems and would additionally have positive implications for fairness, (global) justice, privacy, transparency, and accountability.

New technologies have the potential to affect the environment in disruptive ways. In the emergence phase of new technologies, however, these impacts are often unclear or underestimated. 

One of the main tasks of technology ethics is to proactively identify the potential harms of emerging technologies and possible ways to minimize them. Typical values considered by technology ethics include respect for human dignity and autonomy, beneficence, avoidance of harm, fairness, justice, and privacy. 

Environmental sustainability is mainly discussed in relation to specific technologies (such as nuclear power and molecular biology) and application areas (transport, energy production, agriculture, etc.), but the relevance that ethical evaluations attach to it varies greatly across different technology fields.

In the field of AI ethics, sensitivity to environmental sustainability is still emerging. Environmental sustainability is mentioned only cursorily in the draft EU AI Act, and a global quantitative survey of existing AI guidelines published in 2019 found that sustainability is mentioned in only 14 of the 84 guidelines examined. The recent European Guidelines for Trustworthy AI and the UNESCO Recommendation on the Ethics of AI acknowledge sustainability, along with societal well-being, as one of the key requirements that should guide the production and use of AI artifacts. 

But even when sustainability is considered, AI ethics is still partly characterized by a short-term focus on the reliability of AI products, as opposed to a long-term focus on the impact of technological innovation, especially if compared to technology fields where sustainability is an established principle. 

Moreover, AI ethics focuses mainly on the immediate impact of AI applications on their users, rather than taking a broader perspective on stakeholders that also includes society at large and future generations. AI ethics could therefore benefit from a long-term, broad-based approach that is more open to stimulating reflection on sustainability issues.

By strengthening its focus on sustainability, the ethics of AI would be able to acknowledge the enormous environmental impact of AI. Rare minerals, land, and water are needed in huge quantities to build the special chips and other hardware components that are essential for AI systems and to house and cool the servers that store and process the data used to train AI systems. In addition, the development and maintenance of AI systems generate large amounts of carbon emissions, and, at the end of their life cycle, the hardware components leave behind waste that in turn requires land for storage.

Focusing on the carbon footprint of AI systems, a recent MIT study calculated that one training session of a large language model (which is only one of many steps in the development of an AI system) generates about 284 tons of CO2. This is equivalent to five times the carbon footprint of an entire car’s life cycle, including fuel consumption, and about 57 times the amount of CO2 emitted by an average person in a year.