For the time being, many AI-related processes, such as machine learning, deep learning and training large language models, are very energy-intensive operations.
The main message of most environmental impact calculations is that systems that require high computational power can be a factor that significantly exacerbates the climate and environmental crisis if the consumed electricity is produced from fossil fuel.
One significant environmental and ethical burden related to IT devices are the minerals required in their components, often mined without paying any heed to environmental impacts. People in these mines are often underpaid and operate under dangerous working conditions. These worksites may also use child labour.
Many minerals are also mined in countries whose population does not ultimately benefit from the work they do, as their rate of digitalisation is very low. The fruits of their labour are consumed in post-industrial countries.
Public sector organisations should be well equipped and willing to take these factors into account when making procurements. Developing environmental certificates as procurement criteria for AI systems would be a major step forward. Before this becomes reality, each organisation must strive to make conscious and responsible decisions independently.