Did you know that the carbon footprint from using one AI model can reach 284 tons of carbon dioxide, or about 5 times the lifetime emissions of an average car?

Artificial intelligence (AI) offers tremendous opportunities for the business world, but its use also comes with great responsibility. AI-based systems have a significant impact on people’s lives, raising important questions about ethics, data governance, trust, and legality. As a company delegates more decisions to AI, it assumes potential risks in terms of reputation, data privacy, and liability, whether related to health, safety, or employment. According to an Accenture study, 88% of people surveyed do not trust decisions made by AI. Additionally, Europe seeks to play a leading role in the world in terms of AI regulation and governance.

Responsible AI is the practice of designing, developing, and applying artificial intelligence to enhance employee and business capabilities, while positively impacting customers and society as a whole. This is an important condition for building trust and deploying AI at scale in a calm manner.

Green AI

Some countries are exploring similar topics, but with different perspectives, such as India which is taking a unique approach to responsible AI. India may be placing less emphasis on regulation and more focus on data governance and privacy, but it is showing strong interest in green AI. Green AI is dedicated to creating energy efficient, environmentally friendly and sustainable algorithms. He also focuses on applying AI to combat climate change, address food production and food security, and explore other ways in which AI can have a positive impact on life on earth. .

More energy-efficient AI approaches still require significant sacrifices in AI model quality. For example, researchers at Google and the University of California at Berkeley have shown that the carbon footprint of large language models can be reduced 100 to 1,000 times with the choice of algorithms, specialized hardware, and energy-efficient cloud data centers. Accenture Labs researchers found that training an AI model on a 70% complete data set reduced its accuracy by less than 1%, but reduced power consumption by 47%.

Data Science

Green AI is a topic for example for Accenture in India with their lab which is an impressive facility housing almost 4000 people working on innovation, helping Fortune 500 companies and other organizations move to AI. It also acquired industrial artificial intelligence company Flutura. The startup will power Accenture’s industrial AI services to improve factory, refinery and supply chain performance while enabling clients to achieve their net zero goals more quickly. Its AI platform provides a self-service solution for advanced analytics. This solution helps process engineering, asset management, and reliability teams assess, predict, and improve the performance, reliability, throughput, and energy efficiency of production and manufacturing facilities.

Accenture made many acquisitions to increase its portfolio around AI such as Albert in Japan. Analytics8 in Australia, Sentelis in France, Bridgei2i and Byte Prophecy in India, Pragsis Bidoop in Spain, Mudano in the UK, and Clarity Insights, End-to-End Analytics and Core Compete in the United States. The goal is to master the art of distinguishing signal from noise. Data, analytics and AI open up new possibilities for businesses looking to grow and differentiate themselves. Today, leaders are looking for ways to optimize resource use, increase efficiency, develop new revenue streams and create new business models. All the transformations that produce these values ​​have one thing in common: data.

Transformation through data requires recognition of the value of human, financial, or intellectual resources, which have long been considered critical elements in business growth for several centuries. This is one of the keys to environmentally friendly AI!