Perhaps one of the best technology industry equivalents to the rise of environmental, social, and governance (ESG) investing is the sharp ascendancy of artificial intelligence (AI).
Built around complex algorithms that help powerful computers learn to predict patterns, AI is expected to play a critical role in 77% of the financial services businesses surveyed in the 2020 World Economic Forum report Transforming Paradigms: A Global AI in Financial Services Survey.
Those polled anticipate high levels of deployment in sustainable investing, where AI can help make sense of large volumes of ESG data from myriad sources. However, trade-offs inherent in the advancing technology may raise risks that diminish expected advantages.
Bolstering ESG Analysis
Given the tireless nature and virtually limitless boundaries of AI, it could be positioned as a check against potential greenwashing by companies claiming to act more responsibly than they actually are. Helping address gray areas created by a lack of disclosure standards, the technology reaches out to external data sources to reinforce, validate, or discredit companies’ self-reporting.
Data-driven analysis gains an additional edge with natural language processing, which discerns key trends and developments from text-based sources. Insights pulled from news stories and reports can highlight emerging trends that may qualitatively affect a company’s finances beyond what is featured on income statements, balance sheets, or cash flow statements.
In addition, sentiment analysis may be layered onto textual assessments to further assess a company’s perceptions of trends and developments. Initially applied to published reports, the technology has evolved to analyze audio recordings such as quarterly analyst calls.
Ultimately, the ability to pull intelligence from various sources’ unstructured data can sharpen the transparency and accuracy of ESG reporting, according to Tech Monitor. The volume of such information grew more than sixfold between 2014 and 2020, making the ability to capture, process, and analyze it in a timely fashion essential.
Increasing Corporations’ ESG Sophistication
Over the last decade, corporate ESG reporting has swung into the mainstream. Ninety percent of companies in the S&P 500 Index published sustainability reports in 2019, marking an impressive jump from the 20% that made such disclosures in 2011.
This rise in prominence has also led to heightened scrutiny. Companies turn to AI to explore unstructured data resources that may have been brushed over in the past due to researchers’ biases or limited resources.
Once they have tapped deeper data reservoirs, companies may conduct more extensive risk assessments. These may take various forms, including validating corporate social responsibility claims and anticipating the impact of further climate change. Both strategies strengthen an organization’s ability to respond to future unknowns.
Global consulting firm EY predicts such nontraditional data sources and analysis will eventually drive the rise of measurements around trust, culture, and ESG risks in assessing an organization’s long-term value prospects. Companies that adopt such stakeholder-focused perspectives hold higher promise for improved future value than those that remain focused solely on traditional financial metrics.
Addressing Areas for Improvement
Despite AI’s potential, the World Economic Forum has reported on its possible downsides as well. Issues with facial recognition systems, automated decision-making, and COVID-19 tracking programs underscore the need for the ethical development and implementation of AI to earn acceptance from broad population groups and governments.
The organization added that AI could further global inequality as a byproduct of a lack of diversity within the tech community. Men represent 80% of AI professors; less than 5% of employees at leading technology firms are people of color, and North America, Europe, and China stand to reap 80% of the economic benefits of AI. In response, the World Economic Forum created the Global AI Action Alliance to improve real-time collaboration across new partnerships worldwide and raise the trustworthiness, transparency, and inclusivity of AI systems.
The core issue is what AI learns. If it simply replicates limited perspectives restricted to present-day mindsets, an article in the Harvard Gazette concludes that the technology could face ethical conundrums including issues around privacy, surveillance, bias and discrimination, and even how it frames the role of human judgment.
Meanwhile, IR Magazine notes that the risks associated with company-level AI are those that exist in any relationship with third-party technology or firms that use the technology without adequate policies against unethical behavior. Furthermore, the sizable environmental and financial impact of running high-powered computer systems cannot be ignored, Cornell University researchers found.
AI’s pros and cons are more fluid concerning its impact on the workforce. Alarms about automation-related job losses are countered by the World Economic Forum’s projections of expanded opportunities, provided companies and governments prepare workers for an AI-fueled future.
Setting the Stage for a Steadily Expanding Role
Scanning the horizon, global consulting firm Deloitte sees the sharp growth of ESG-focused investments leading to broader implementation of AI technology among asset managers, especially those looking to gain an edge on their competitors. As such investor demands align with those of other stakeholders, savvy corporate executives will ideally press their organizations in turn to meet rising ESG expectations and improve long-term value outlooks.
The increased integration of AI analysis will not be without fits, starts, and occasional setbacks. Yet if its practitioners remain cognizant of identifying and addressing its downsides, it appears well-armed to strengthen ESG data.