The importance of social and environmental responsibility in AI systems
The development and use of AI should take into account the broader social and environmental impacts, and strive to promote sustainability and social responsibility. This is because AI can create a new technological utopia that obscures the way that AI can aggravate environmental degradation, usually in ways that harm marginalized populations.
AI has been heralded as an influential tool for combating climate change. For instance, self-driving cars may bring down emissions by 50% by 2050. Using AI in agriculture leads to higher yields. For instance, farmers within India have attained a 30% larger harvest through the use of AI technology.
The other tangible evidence is the ability of AI to provide faster and more accurate analysis of satellite images that identify areas stricken by disasters in need of help or rainforest destruction.
Description of how AI can harm the environment
Despite its benefits, the broader communications and internet industry where AI operates have been heavily criticized for its use of excessive quantities of energy. For instance, the supercomputers used for running AI programs are powered by the public electricity grid with support from diesel-powered generators. It is estimated that training one system can lead to the emission of 250,000 pounds of CO2.
As a matter of fact, using AI technology across all sectors yields carbon dioxide emissions almost equivalent to that of the aviation industry. Such additional emissions disproportionately affect historically marginalized communities. These communities often live in areas that are heavily populated due to increased industrial zoning (a symbol of environmental injustice) and are more directly impacted by pollution’s health hazards.
What this means is that AI developers should consider the social and environmental impact of their technology because of its potential to substantially increase energy consumption and GHG emissions, especially if not deployed efficiently. This could aggravate the already worsening climate crisis, making it even more pertinent to address AI’s environmental impact.
AI developers should aim to develop AI that has a positive impact on society and the environment. There is a rising recognition that AI can play a vital role in improving sustainability efforts. Through the identification of sources of inefficiency and waste in both infrastructural and product development, sustainable AI can play a critical role in enabling firms to bring down their environmental footprint.
There are already attempts geared towards using more green energy in the development of AI infrastructure, with firms considering the use of renewable sources of energy to power data centers. Socially and environmentally sustainable AI can also help to tackle environmental challenges more effectively and proactively. For instance, illegal fishing and deforestation can be effectively identified and responded to using AI.
As the world looks to a better future, AI will increasingly be deployed to tackle environmental challenges beyond only boosting corporate profits. It is worth noting that sustainable AI is important for a sustainable future for everyone.
Examples of how AI can have a positive impact on society and the environment
Monitoring Deforestation:
- The Global Forest Watch deploys AI and machine learning algorithms for analyzing satellite imagery and tracking forest cover changes. The platform plays a crucial role in identifying areas vulnerable to deforestation and allows conservationists and policymakers to take timely action.
- Brazil’s Rainforest Connection has developed a system that utilizes AI together with acoustic sensors to not only detect but also report chainsaw noises, commonly utilized in illegal logging. The technology could be pivotal for reducing deforestation within the Amazon rainforest by up to 71% within some areas.
Water Management and Detection of Illegal Fishing:
- Illegal fishing threatens marine ecosystems, and AI is being utilized for detecting and preventing such activities. For instance, the OceanMind project makes use of vessel tracking data, satellite imagery, and AI algorithms for detecting fishing activities and identifying unreported, illegal, and unregulated fishing vessels. This project has been instrumental in reducing illegal fishing within Southeast Asia and West Africa waters.
- The Global Fishing Watch Project deploys machine learning and AI algorithms for analyzing the movement of vessels and tracking fishing activities in real time. Through the platform, authorities can detect and deter illegal fishing activities, hence safeguarding marine ecosystems and ensuring that fish stocks are sustainable.
Sustainable Agriculture:
AI can be used to reduce waste and maximize agricultural practices. For instance, the startup Taranis utilizes AI and computer vision for analyzing crop images and identifying potential issues, including diseases, pests, and nutrient deficiencies.
Construction and Commercial Buildings:
AI is also deployed for improving energy efficiency and optimizing renewable energy systems. An example is the Verdigris startup that uses AI algorithms for analyzing energy consumption patterns within commercial buildings and identifying areas where it is possible to reduce waste. Businesses have reduced their energy bills by 20% using this technology.
Within the construction sector, it can help minimize building materials’ carbon footprint by identifying more sustainable alternatives and reducing construction’s environmental impact.
Recommendations for ensuring social and environmental responsibility in AI systems
A number of renewable energy solutions, cutting-edge initiatives, and energy-efficient hardware designs can revolutionize the AI industry while minimizing its environmental harm.
- Using renewable energy for powering data centers. For instance, Microsoft and Google have committed to using 100% renewable energy for powering their data centers, minimizing their carbon footprint.
- Deploying more energy-efficient hardware for AI. It includes the design of AI-specific chips that consume less power and optimizes software for reducing energy consumption. The approach can significantly minimize the energy needed for running AI apps, reducing the carbon footprint.
- Edge computing for reducing AI’s energy consumption. Edge computing minimizes the need for data to be transferred from and to centralized servers, reducing latency and consumption of energy.