There is no doubt that the world will change in the future with the transformational impact of artificial intelligence (AI). However, despite its potential for good, AI may also have unfavorable effects on society. The current expansion of AI has led to the emergence of AI ethics due to the availability of ever-increasing volumes of data and processing power.
The initial wave of AI ethics centered on potential uses of AI, such as superintelligence, and was related to the morality of fantastical scenarios like robot revolt. The second wave of AI ethics focused on practical issues with machine learning (ML) techniques, including the black-box algorithm, the explainability issue, the lack of equality in training data resulting in biases in AI models, and the rise of facial and emotion recognition systems violating citizens’ rights, such as privacy. It is now time for the third wave of AI ethics, which should focus on the current environmental catastrophe and actively educate academics, decision-makers, AI developers, and the general public on how AI affects the environment.
The primary focus of the third wave should be sustainable development. It is time to move beyond this and consider the sustainability of creating and utilizing AI systems in and of themselves, even while there is an increasing effort to focus AI usage towards “good” ends, such as sustainable development objectives.
What is Sustainable Artificial Intelligence (AI)?
Artificial intelligence (AI) systems that adhere to ethical business principles are referred to as sustainable AI. While many current AI systems are environmentally unfriendly, workable solutions exist. Those with experience creating and using AI models know that training AI models is a laborious process that requires vast amounts of energy, resulting in unavoidable emissions and air pollution. According to the MIT Technology Review, training just one AI model can result in the lifetime emissions of an average American car being nearly quintupled to over 626,000 pounds of carbon dioxide equivalent.
To reduce energy consumption during the training process, AI models must be made more efficient. All components of an AI system should be optimized to reduce power consumption. By doing so, businesses can develop new methods for AI modeling and optimization that reduce the adverse effects of their AI systems on the environment.
In addition to reducing the environmental impact of AI, there is also a crucial aspect of sustainable AI that focuses on the use of AI and machine learning as tools to support sustainable development in various industries. This involves employing AI and ML to assist organizations in functioning sustainably, ensuring that the future is not jeopardized by today’s successes.
There are two key aspects to sustainable AI. The first is to ensure that AI is itself sustainable and does not cause harm to the planet. The second is to leverage AI as a tool to promote sustainable development and reduce the negative impact of various industries on the environment. By focusing on both aspects of sustainable AI, we can pave the way towards a more environmentally conscious and sustainable future.
Integration of AI in Sustainability Efforts: Survey Findings
Based on the survey conducted by Mckinsey, it appears that numerous organizations which have implemented AI are incorporating AI capabilities into their sustainability initiatives and are actively searching for ways to decrease the environmental impact of their AI use (as indicated in the exhibit).
Among respondents from organizations that have adopted AI, 43% confirmed that their organizations utilize AI to support sustainability efforts, while 40% stated that their organizations are making an effort to decrease the environmental impact of their AI use by reducing the energy consumed during AI model training and operation. As high-performing companies tend to have more advanced AI initiatives and have invested more in AI than others, they are 1.4 times more likely than others to report AI-based sustainability efforts and to mention that their organizations are striving to lower AI-related emissions. Such efforts are more common in organizations located in Greater China, Asia-Pacific, and developing markets, while respondents in North America are the least likely to report them.
Sustainable AI Use Cases from Now and The Future
AI systems can aid sustainability projects related to pollution, natural disasters, and climate change. Here are some examples:
- AI-powered precision agriculture
- Weather forecasting
- Natural disaster response
- Infrastructure enhancement and preparedness, including electricity providers
Drones and other technologies can be combined with AI analytics to monitor and mitigate the impacts of natural disasters like floods. These AI solutions work with Internet of Things sensors to protect crops, infrastructure, and other valuable assets and help monitor climate and risk. Additionally, predictive AI, using historical data and trends, is essential for predicting future emissions and air pollution. Many of these AI-based technologies are also used in reforestation efforts in the Amazon. AI systems can track forests and changes in them over time by analyzing data from sensors, drones, and satellite imagery. ML systems can use historical data to identify potential future deforestation scenarios due to their predictive capabilities.
When properly adopted and carried out, sustainable AI will reduce the harmful environmental consequences that conventional AI and ML model training and optimization practices have without compromising the accuracy of their predictions and outputs. Also, it is used in various use cases to help people defend their local communities and environmental assets. Green AI, which also includes initiatives to mitigate these adverse impacts and ensure the future of AI provides more benefits than drawbacks, is sometimes used interchangeably with sustainable AI.
Artificial Intelligence Applications for Sustainability Strategies
The following are some ways that an enterprise’s sustainability plan might use AI:
- Monitoring and Reporting of Emissions- Analyze and track an organization’s emissions data to pinpoint areas that need improvement.
- Monitoring the supply chain- Analysis and keeping an eye on suppliers’ sustainability practices.
- Predictive upkeep- To minimize unplanned downtime and cut down on energy waste, predict equipment breakdowns and schedule maintenance.
- Intelligent transportation- To reduce emissions from transportation and improve logistics and transportation networks.
- Efficient use of energy- To cut down on energy use and carbon emissions, and optimize HVAC, lighting, and other equipment in buildings.
- Integration of renewable energy- Include renewable energy sources in the grid, such as wind and solar.
- Carbon sequestration and storage- To cut emissions from industrial operations, carbon capture, and storage systems should be designed and operated more efficiently.
Sustainability-focused Use Cases of Artificial Intelligence in Different Industries
Numerous industries, such as manufacturing, mining, tourism, and transportation, can leverage AI to reduce their carbon footprints. While the implementation of AI may initially lead to increased costs, these industries stand to gain significant long-term cost reductions and reap other benefits, such as improved brand awareness and customer loyalty.
Industries are responsible for determining practical sustainability applications.
- Retailers utilize AI to optimize energy consumption in their stores and minimize emissions from delivery vehicles.
- E-commerce businesses employ AI to streamline shipping routes and minimize delivery vehicle emissions.
- Telecom companies rely on AI-based data analysis for predictive maintenance, which can also reduce energy use, downtime, and improve network efficiency.
- Technology businesses use AI to decrease emissions and optimize energy use in data centers.
- AI and analytics are used throughout the food supply chain to forecast demand, prevent waste, and reduce losses.
- The fashion industry leverages AI-driven consumer analysis for demand forecasting and inventory management to reduce waste and avoid sending returned apparel to landfills.
We must consider the environmental impact of AI and machine learning technologies as they continue to be rapidly adopted in our society. Using AI to streamline operations and reduce emissions can help companies make significant progress towards net-zero objectives and a sustainable future. Artificially intelligent systems have caused various disruptions in businesses in recent years, marking a cognitive era where systems can see, hear, react, and learn from every encounter. The next wave of digital transformation solutions will set new standards in the digital realm.
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