How can Artificial Intelligence (AI) contribute to the uprise and boom of Environmental, Social, and Governance (ESG) reporting, analysis, and beyond?
ESG investing, in particular, has been a big part of this boom: Bloomberg Intelligence found that ESG assets are on track to exceed $50 trillion by 2025, representing more than a third of the projected $140.5 trillion in total global assets under management. Meanwhile, ESG reporting has become a top priority that goes beyond ticking off regulatory boxes. It’s used as a tool to attract investors and financing, as well as to meet expectations of today’s consumers and employees.
Certainly, over the past couple of years, ESG issues have soared in importance for corporate stakeholders, with increasing demands from investors, employees and customers. According to S&P Global, in 2022 corporate boards and government leaders “will face rising pressure to demonstrate that they are adequately equipped to understand and oversee ESG issues — from climate change to human rights to social unrest.”
However, according to a recent Oracle ESG global study, 91% of business leaders are currently facing significant challenges in advancing sustainability and ESG initiatives. Finding the right data to track progress, as well as time-consuming manual processes to report on ESG metrics, are examples of these.
According to Christina Shim, VP of strategy and sustainability, AI applications software at IBM, AI can help manage data, glean data insights, operationalize data, and report on it.
“We need to make sure that we’re gathering the mass amounts of data when they’re in completely different silos, that we’re leveraging that data to improve operations within the business, that we’re reporting that data to a variety of stakeholders and against a very confusing landscape of ESG frameworks,” she said.
Although a BlackRock survey found that 92% of S&P companies would be reporting ESG metrics by the end of 2020, 53% of global respondents cited “poor quality or availability of ESG data and analytics” and another 33% cited “poor quality of sustainability investment reporting” as the two biggest barriers to adopting sustainable investing, according to Deloitte.
In addition to the operational challenges associated with ESG, the Oracle study found that 96% of business leaders admit that human bias and emotion frequently distract from the end ESG goals. In fact, 93% of business leaders say they would rather have a bot make sustainability and social decisions than a human.
How can businesses get started with AI and ESG?
Seth Dobrin, chief AI officer at IBM, told VentureBeat (leading platform covering leader in covering transformative tech) that companies should get started now on using AI to harness ESG data. “Don’t wait for additional regulations to come,” he said.
Getting a handle on data is essential as companies begin their journey towards bringing AI technologies into the mix. “You need a baseline to understand where you are, because you can make all the goals and imperatives, you can commit to whatever you want, but until you know where you are, you’re never gonna figure out how to get to where you need to get to,” he said.
Getting a handle on data is essential as companies begin their journey towards bringing AI technologies into the mix. “You need a baseline to understand where you are, because you can make all the goals and imperatives, you can commit to whatever you want, but until you know where you are, you’re never gonna figure out how to get to where you need to get to,” he said. Dobrin said he also sees organizations moving from a defensive, risk management posture around ESG to a proactive approach that is open to AI and other technologies to help.
Here’s an explanation of Responsible AI (RAI) relationship to ESG.
Creating sustainable environmental goals
AI systems may pose a significant threat to sustainability goals due to the heavy computing power needed to train large neural networks. By understanding this impact and instituting practices that prefer smaller models — which often are easier to understand and interpret — companies can potentially reap benefits for their ESG sustainability measures.
Developing a human-centered social environment
A common concern around AI focuses on people. Are they treated fairly, or are existing societal inequities replicating or even amplifying? How is the company using individuals’ data? How is customers’ privacy protected? Is a potential technology purchase designed to be human-centered? What impact have new technologies had on stakeholders, including customers, employees and society?
Taking a holistic outlook toward governance
Instituting a holistic approach to governance involves processes, policies and standards. It also aligns with development team members who promote governance that is tech-enabled rather than simply tech-first. Effective governance of AI manages for impact by considering shifting regulatory requirements and emerging organizational approaches. Responsible technology practices require effective and agile governance — both within an organization and across the regulatory and public policy landscape.
Collaborate with development teams to align on governance
Effective governance addresses risks and harms without stifling innovation. This balance can be difficult to achieve, which is why engaging with the teams that will be governed — specifically, those creating technology for internal or external consumption so they understand the benefits of governance and can right-size it to address the need — goes a long way toward achieving adoption. For instance, helping development teams understand the carbon footprint of the models they are building may push these teams to consider architectures that are simpler, more environmentally friendly and potentially easier to explain.
To tackle this scenario and provide a realistic approach, Hydrus.ai was started in 2019 and positions itself as a first mover. With a mission “Help every business accomplish their climate-related goals” we provide We designed a solution to help all companies take action in a personalized and effective way. Design software that holds companies accountable to their sustainability targets. Streamline the data collection and reporting process so companies can take action against climate change instead of getting stuck in the current manual model.