Unlocking Responsible AI for ESG: Ways AI Can Drive Sustainability and Social Impact
Akbar Mohammed
Principal Architect, Strategic Center
Principal Architect Akbar Mohammed leads readers through the positive impact of AI solutions on global sustainability.
Corporate social responsibility (CSR) is gaining significance in shaping enterprises’ sociocultural, environmental, political, and ethical structure and brand identity. Hence, businesses seek to integrate sustainable practices into their operations for long-term, well-rounded growth.
One way this is being achieved is through artificial intelligence (AI). This powerful tool helps enterprises improve their environmental, social, and governance (ESG) performance, enabling them to monitor and manage their impact on the environment, society, and stakeholders more effectively.
Enterprises worldwide are already taking internal measures to address their environmental impact, such as implementing separate bins for food and plastic and factoring in employee flight travel to calculate their carbon footprint. Some industry leaders have gone even further by committing to net zero carbon emissions. Other companies are taking positive steps toward aligning their ESG initiatives with the UN’s Sustainable Development Goals (SDGs).
The application of artificial intelligence in environmental, social, and corporate governance represents a formidable force for positive change.
While large enterprises have started incorporating data governance and structures to measure their impact, the challenges facing ESG are unfortunately not only many but also complex. Conflicting global policies, varying levels of integration, and the challenges of balancing short-term financial interests with long-term environmental and governance goals indicate that enterprises are still near the beginning of their journey toward achieving ESG compliance.
Many existing AI applications can be reconfigured to identify, monitor, and produce solutions for these challenges. Yet, despite its potential, AI needs to be more leveraged and remains untapped in measuring the impact of sustainability efforts and developing new approaches for more environmentally sustainable practices.
Environmental Impact
The impact of AI on the environmental facet of ESG has significant potential. AI technology is already being used to track greenhouse gas emissions, resource consumption, and weather changes. This data is then analyzed to provide insights that can help conserve natural resources and assist organizations in adopting more environmentally friendly practices.
In the agricultural industry, for example, AI is helping inform decisions regarding adopting new approaches, such as regenerative agricultural practices, to promote environmental sustainability. This involves farmers using the land to a point before shifting to a more sustainable practice while regenerating the previously exploited land for future use.
Social Impact
AI also plays a significant role in measuring and improving employee welfare in the workplace. Fractal implements AI to measure employee well-being through an in-house AI conversational chatbot that keeps a tab on the psychological well-being of employees. Questions are formulated, and a qualified team of specialists interprets results to ensure the human element remains the driving force behind the conclusions. Automated surveys are then released to specific employees based on their work hours, allowing for a more comprehensive analysis.
In addition to monitoring an organization’s internal staff assets, AI can also be deployed to measure and uphold social sustainability practices, such as promoting a safe and fair working environment for employees. Think, for example, of a factory: are working conditions safe? Are female workers fairly compensated for their work compared to their male counterparts? Are the factory’s vendors complying with regulations? AI can be leveraged to answer these questions by conducting large-scale pattern recognition and comparing the results with predetermined metrics.
Impact on Governance
The impact of AI on governance is critical, as it can enhance transparency and accountability to stakeholders and the public.
AI can also be harnessed to measure corruption through various practices, such as monitoring how companies work with vendors, countries, and communities. Companies can even assess their compliance with human rights and corporate governance standards. Both financial and non-financial measures can be tracked to determine the integrity of a company’s governance structure.
Data and data governance
AI can help synthesize data from multiple sources and provide decision-making tools to make it easier to manage sustainability problems such as transparency, assessing ESG compliance, optimizing operations, capturing sensor data, and performing scenario planning simulations. Collecting new and more consistent and reliable data can also be automated, which helps to simulate scenarios and automate standardization, creating more efficiency in ESG reporting and analysis.
There are three broad areas where data can be leveraged with AI.
Descriptive analytics
Predictive analytics
Image recognition
Summarizing existing data to understand where a company stands in terms of ESG.
Predicting future energy consumption, carbon emissions, and alternate sources that can be utilized.
Observing, classifying, and annotating images to track resource depletion and plan for future resource utilization.
Descriptive analytics
Summarizing existing data to understand where a company stands in terms of ESG.
Predictive analytics
Predicting future energy consumption, carbon emissions, and alternate sources that can be utilized.
Image recognition
Observing, classifying, and annotating images to track resource depletion and plan for future resource utilization.
But, with big data comes great responsibility, and data governance is just as critical in ESG operations as anywhere else. It must be reliable, consistent, high quality, and compliant with existing regulations. Therefore, robust data governance is essential for organizations to utilize ESG policies and make informed decisions efficiently.
Challenges in ESG AI
At the intersection of AI, ESG, and technology lies a complex web of challenges that must be addressed to ensure responsible and sustainable decision-making. These include the following:
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The complexity of measuring and reporting sustainability and ethical practices. Every company has a unique set of operations, and there is no one standard to follow, making it difficult to prioritize and compare.
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A lack of standardization across different ESG metrics, as each global region has its own set of regulations and policies.
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Accountability, as it is not always clear who should be held responsible in the short term.
However, the biggest challenge facing AI in ESG is the environmental footprint that technology creates. For example, developing large language models consumes significant energy and emits substantial carbon emissions. While large tech companies have attempted to implement carbon-neutral policies, there are miles to go to achieve a balance.
Implementing AI in your ESG initiative: Start here
Regarding ESG practices and policies, every organization is at a different stage of development. The first step for those without a framework is forming an ESG strategy to guide decision-making.
The next challenge is measuring the organization’s ESG framework and implementing programs that will make a meaningful impact.
Finally, there is the execution phase — capturing data, building infrastructure, and focusing on analytics and personnel to support ESG initiatives.
AI is just a tool – to harness its power, we must combine behavioral science, design, and engineering to create a diverse, responsible, and sustainable solution for clients.
Specific solutions will vary depending on an organization’s ESG journey. But the goal remains the same: to create a sustainable and responsible decision-making approach that benefits the organization and the wider world.
Moving toward a sustainable future
AI technology in ESG has significant potential for growth and development, which can lead humanity to a place of sustainability and positive social impact. For instance:
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Large language models can be used to understand public concerns and provide potential remedies.
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Machine vision can be applied to analyze satellite imagery, photos, and videos to aid in environmental impact measurement and compliance.
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AI-powered smart building and energy management systems can help reduce energy consumption and minimize waste.
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Predictive analytics can be deployed to identify patterns and trends in historical data to help anticipate future ESG risks and opportunities.
Since there is no standard guideline for ESG solutions, companies can explore different approaches to solve the problems we face. However, the journey toward sustainability is a global issue, and organizations need help to embark successfully on it. Policymakers and other stakeholders must actively engage in promoting positive change. While the impact of technology on ESG issues is yet to be seen, the positive momentum from organizations and governments’ response to the climate crisis and other ESG issues is promising and points towards a better, more sustainable future.
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