As we reflect on the year 2023, it's important to highlight the most significant ESG controversies that made headlines. Our last article in this series focused on the environmental aspect. This time, we turn our attention to the social pillar of ESG, focusing on issues such as strikes, layoffs, human rights violations, and discrimination against minority groups. We emphasize the need for accountability and action to address these pressing social issues and promote social responsibility.

Social Risks: Focus 2023

In 2023, social risks were the most significant, with layoffs and strikes gaining significant attention. It's crucial to acknowledge these social risks and take accountability and action to address them, as they underscore the urgent issues facing society.

Social Controversies of 2023

Social risks have taken the forefront in 2023, with notable web mentions increasing significantly. Here are the most relevant controversial topics:

Social Dialogue

Social discourse intensified at the start of the year, with news of widespread strikes in various sectors, including aviation and education, primarily driven by pay disputes. The wave of layoffs in several tech companies was the talk of the town, especially during the first quarter of the year.

Respect for Human Rights

The year saw an uptick in mentions related to human rights, especially controversies surrounding racism, data privacy violations, sexual harassment, and breaches of GDPR.

Diversity and Inclusion

Discrimination against minority groups, including the LGBTQ community and people of color, and age-based discrimination became a significant topic of discussion in 2023.

Top 5 Social Controversies

These controversies are ranked by relative volume*.

• McDonald's
  

Volume of mentions: 8,903

Relative volume: 87%

McDonald's faced substantial social risks in 2023 due to significant layoffs of its corporate staff in April. The move led to public concern and discussions around the company's employment practices and stability. (source)

• Google
  

Volume of mentions: 13,504

Relative volume: 43%

Google found itself in the spotlight as it faced challenges related to major layoffs in January and October of 2023. These layoffs contributed to almost half of the social risk mentions associated with the tech giant. (source)

• Meta
  

Volume of mentions: 10,965

Relative volume: 38%

Meta, formerly known as Facebook, also faced scrutiny as 38% of the company's social risk mentions revolved around layoffs that took place in March and October 2023. (source)

• Microsoft
  

Volume of mentions: 6,060

Relative volume: 28%

Microsoft faced challenges due to disruptions caused by cyberattacks in early June. In addition, the company had to navigate through controversies related to layoffs, contributing to its social risks. (source)

• X (formerly Twitter)
  

Volume of mentions: 7,246

Relative volume: 8%

X/Twitter experienced a global outage, which was followed by significant layoffs. These events led to considerable public discussions and social risks for the company. (source)

Conclusion

In summary, environmental risks remain a major concern for ESG, but the social pillar of ESG has become increasingly critical, especially in 2023. As we move forward, it's important for companies to acknowledge and address social risks, such as layoffs, strikes, human rights violations, and diversity and inclusion issues. By promoting social responsibility, companies can make a positive impact on society, create a more sustainable future, and enhance their reputation as socially responsible organizations.

Click here to learn about the top environmental and governance controversies in 2023.

Relative volume*: Relative to the total volume of E, S, or G risks for the company during the same period.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

As Climate Week NYC approaches, artificial intelligence is emerging as a transformative force in sustainability reporting and ESG practices. This year's event preparations signal a fundamental shift from traditional manual processes to AI-powered solutions that promise to revolutionize how companies track, analyze, and report their environmental impact.

The Technology Focus

Among the notable events planned is "How AI is Disrupting Sustainability Reporting," hosted by Sustainserv in partnership with leading technology and sustainability firms, scheduled for September 23, 2025. This event exemplifies the industry's growing recognition that AI technologies may be key to solving persistent sustainability measurement and reporting challenges.

The focus on AI reflects urgent industry needs. Traditional ESG reporting has long struggled with data collection complexity, accuracy concerns, and the challenge of tracking Scope 3 emissions across global supply chains. Manual processes are proving inadequate for the scale and sophistication required by modern sustainability commitments.

AI's Transformative Applications

AI is addressing these challenges through several breakthrough applications. Advanced machine learning algorithms can now automatically extract and validate data from multiple sources, providing real-time monitoring of environmental metrics and cross-referencing information to identify inconsistencies that might indicate greenwashing.

Perhaps most significantly, AI is revolutionizing Scope 3 emissions tracking, the most complex aspect of carbon accounting. Machine learning algorithms can map intricate supplier networks, automatically calculate emissions factors, and use predictive modeling to estimate emissions even when supplier data is incomplete.

Computer vision technologies are opening new frontiers in environmental monitoring, from satellite imagery analysis for deforestation tracking to automated waste sorting optimization. Natural language processing enables automated analysis of sustainability reports and regulatory compliance monitoring.

Investment Implications

For investors, this AI-ESG convergence represents both opportunity and transformation. Enhanced due diligence capabilities allow for more sophisticated ESG analysis, including automated screening of potential investments and real-time monitoring of portfolio companies' sustainability performance.

The integration also creates new investment themes, from ESG technology companies developing AI solutions to traditional software companies pivoting to sustainability applications. Asset managers benefit from reduced costs for ESG research, faster regulatory response times, and improved accuracy in risk assessment.

Challenges and Considerations

Despite the promise, AI integration faces significant challenges. Data quality remains a concern, as AI systems are only as good as their underlying data. Historical ESG data may contain biases, and algorithmic bias could perpetuate existing inequalities.

Regulatory uncertainty adds complexity, with unclear guidelines on AI use in ESG reporting and potential liability issues for AI-generated recommendations. Implementation requires substantial organizational change, including staff training and system integration.

Looking Forward

The prominence of AI-focused events at Climate Week NYC signals that the sustainability industry is entering a new technological era. As AI continues to mature, the industry is likely to see more accurate, timely, and comprehensive sustainability data.

This evolution could accelerate the transition to more sustainable business practices by making environmental and social impacts more visible and actionable. However, success will ultimately be measured not by technological sophistication alone, but by the ability to drive real-world improvements in environmental and social outcomes.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

By Magnus Billing, SESAMm advisor, with insights from Sylvain Forté, CEO of SESAMm

Investors have faced so-called “black swan” events throughout history: unexpected crises with severe consequences, often rationalized only in hindsight. Yet in an era defined by generative AI and vast, real-time data lakes, the question arises: could such events be understood and acted upon before they unfold?

The 2023 U.S. regional banking crisis offers a striking case study. The rapid collapses of Silicon Valley Bank and Signature Bank revealed how quickly stress can spread and how difficult it remains to connect early warning signs across sources.

While traditional financial analysis focuses on fundamentals such as capital ratios, liquidity positions, governance, and earnings, a new class of tools is expanding the lens. AI-driven controversy data aggregates and analyzes millions of public sources, from regulatory statements to media and industry discussions, to detect emerging issues as they surface. It does not replace quantitative and fundamental analysis; it complements it by tracking the visibility of risk as it enters public conversation.

This combination of approaches may offer investors a fuller picture: the structural risks visible in balance sheets, and the narrative risks revealed through public dialogue. To test this idea, we revisited the 2023 crisis through both perspectives, starting with what traditional analysis could have shown and what it missed.

Traditional Analysis and Its Blind Spots

In hindsight, the vulnerabilities of regional banks such as Silicon Valley Bank and Signature Bank were visible before the start of 2023. Unrealized losses on long-term securities, heavy reliance on uninsured deposits, and exposure to interest-rate risk pointed to potential liquidity stress. Yet these indicators were neither fully recognized nor connected in the market.

Traditional analysis has a tendency to evaluate banks based on their specific niches: Silicon Valley Bank focused on technology and venture financing, while Signature Bank served commercial real estate and digital asset clients. However, this approach risks overlooking the common and shared structural factors: concentrated depositor bases, high sensitivity to interest rate changes, rapid growth, and weaknesses in governance. Few, if any, observers recognized how rapidly these vulnerabilities could interact and escalate in a modern, digitalized banking environment.

While financial reports contained the data, there was little discussion connecting these risks in the public domain. But what about controversy data? Would it have caught the impending crisis? To find out, I asked Sylvain Forté, CEO of SESAMm, to provide an AI perspective.

What the Data Showed: Signature Bank

Signature Bank displayed a gradual pattern of emerging risk visible through public discussion. From mid-2022 onward, controversy data showed a rise in coverage related to governance practices, management oversight, and deposit concentration risks, often in the context of its ties to the digital-asset industry.

Importantly, it was not the crypto exposure itself that led to the bank’s collapse. The bank even announced in December 2022 that it would reduce its crypto-related business. Instead, the FDIC’s Supervision of Signature Bank report concluded that, “the root cause of SBNY’s failure was poor management. SBNY’s board of directors and management pursued rapid, unrestrained growth without developing and maintaining adequate risk management practices and controls.”

From a controversy perspective, those signals were publicly visible but fragmented. As shown in the chart above, AI-powered monitoring could have aggregated them into a clear view of a sustained drift in governance-related discussions, offering an early indication that oversight and internal controls were under pressure and risk was increasing.

What the Data Missed: Silicon Valley Bank

In contrast, Silicon Valley Bank presented a markedly different pattern. While controversy data registered some activity in late 2022,  including investor reactions to financial forecasts and coverage of routine business operations, these signals were fundamentally different in character from Signature Bank's governance-related warnings.

The September 2022 increase reflected market disappointment with financial guidance rather than operational or governance concerns. The subsequent activity captured normal business news, such as arranging syndicated loans. Critically, there was minimal public discussion of the bank's balance-sheet structure, unrealized losses, or depositor concentration risk until the crisis was already unfolding in March 2023.

This example underscores a key distinction: AI controversy monitoring excels at capturing reputational, governance, and operational risks as they enter public dialogue, but may not surface structural financial risks that remain confined to regulatory filings and analyst reports.

Lessons from Both Cases

The contrast between these two banks illustrates the complementary roles of quantitative and fundamental financial analysis vs AI-driven controversy monitoring.

In Signature Bank’s case, controversy data captured a steady accumulation of governance-related warnings, a slow build-up of risk visible through public discussion.

In Silicon Valley Bank’s case, the risks were structural but not yet discussed, leaving little for AI-powered controversy data to detect.

As Sylvain explains, “AI controversy monitoring helps investors understand how and when risks start to emerge in public dialogue. It does not replace fundamental analysis. It complements it by showing when the conversation begins to shift.”

Conclusion

Black swan events are often rationalized only in hindsight, but the 2023 regional banking crisis suggests a more nuanced reality. Some signals existed. What remained difficult was connecting them across sources before stress became contagion.

AI-driven controversy monitoring proved effective at surfacing governance and operational risks as they entered public dialogue, as Signature Bank demonstrated. Yet structural financial vulnerabilities like those at Silicon Valley Bank may not generate discussion until crisis forces the conversation, underscoring that no single lens captures all risk.

The advantage lies not in prediction, but in preparation: combining the structural risks visible in balance sheets with the narrative risks revealed through public discourse. In an era of real-time data and generative AI, the question is no longer whether information exists, but whether investors can connect it before it becomes consensus.

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