The Environmental Costs of AI

AI’s mass-market adoption promises many positive things however, it is important to be aware of the environmental costs of integrating AI into our daily lives.

The widespread use of AI will have the following environmental costs:

• High energy consumption: AI algorithms are complex and require a lot of energy to execute. Data models supporting these algorithms rely on intensive computational power. This requirement can be persistent and increase as more AI models are trained to reduce error rates and make the models more responsive.
• Significant infrastructural emissions: Advanced infrastructure is required to support AI models at a scale that supports millions of users. The cost of data centres housing this infrastructure, and their additional cooling and emission management requirements would create significant environmental debt in terms of water requirements and increase of greenhouse gases emitted.
• Increase of E-waste: With the rise of AI, older technologies would become obsolete quickly. The machines previously needed to run this technology would soon be disposed of, leading to an increase in E-waste load, causing an additional burden for the environment. E-waste contains hazardous materials like lead, mercury, and cadmium, which can harm the environment and human health.
• Discrimination due to bias: AI models have a risk of becoming biased if trained with too similar data sets. Since these AI models when deployed for millions of users can have high-speed responses that drive engagement and adoption on a massive scale, by the time the regulatory bodies catch up to determine and independently evaluate these biases, it could already be very late. These biased models could then perpetuate incorrect narratives that could create discrimination and gross environmental injustice.
• Lack of regulations: Since there are no solid regulations in place for AI development and use (we are still catching up in terms of data privacy and data integrity issues — the concerns of the previous generation of technologies), this creates a lack of transparency. This could lead to difficulties in addressing and fixing the environmental impact that AI models could have on the environment.

In conclusion, while AI has the potential to benefit humanity in many ways, it’s critical to be aware of the potential environmental costs of its use. AI can have a huge environmental impact on everything from data centre emissions and bias to energy use and e-waste.

Energy efficiency improvements, e-waste reduction efforts, and transparency and accountability mechanisms in AI development and use are all critical for minimising these effects.

By doing this, we can make sure that AI is applied in a way that is good for both the environment and society.