[Student IDEAS] by Hugo Jalet - Global BBA at ESSEC Business School
The rapid expansion of ESG data and divergent rating methodologies have undermined the credibility and consistency of sustainability scores, enabling greenwashing and investor confusion. While the EU’s CSRD introduces reporting standards, it stops short of unifying rating criteria. Artificial intelligence (AI) offers tools to structure ESG assessments, detect inconsistencies, and enhance transparency. However, AI faces limitations: data biases, opacity, interpretive challenges, and significant environmental costs. Effective ESG scoring requires a hybrid model combining AI capabilities with human oversight, regulatory standards, and open-source approaches. Used responsibly, AI can support more reliable, transparent, and sustainable ESG evaluation systems—without replacing human judgment.
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The rise of environmental, social and governance (ESG) criteria has transformed the way companies are assessed, both by governments and by banks and other finance companies. However, this development has also led to a significant ESG data explosion, compounded by a lack of harmonized ratings. Although the Corporate Sustainability Reporting Directive (CSRD) standards, which came into force in Europe at the beginning of the year, are intended to provide issuers with a more precise framework, their precision will not automatically lead to standardization and properly consolidated reporting. This fragmentation and non-standardization of data create fertile ground for greenwashing, which essentially means falsely portraying something as environmentally friendly, with some companies artificially embellishing their ESG performance without any real commitment to sustainability.
Artificial intelligence is often presented as a solution to these challenges. According to Del Vitto (2023), advances in machine learning make it now possible to analyze vast sets of ESG data, identify the decisive criteria in assigning scores and make the decisions of rating agencies more explicable . In addition, thanks to AI, it is also possible to track down inconsistencies between companies' declarations and their actual actions, thus contributing to the fight against greenwashing (Moodaley et al., 2023). AI is therefore a powerful yet imperfect tool for structuring ESG ratings. It carries significant limitations: it can reinforce existing biases, lacks full transparency, and demands considerable energy resources. Moreover, most real-world applications of AI in ESG continue to depend on human auditors to validate findings, making the prospect of a fully autonomous ESG rating system improbable in the near term.
The central question remains: can AI genuinely harmonize ESG ratings and establish a more coherent framework, or does it risk reinforcing confusion by adding another layer of opacity? The answer likely lies in the balance between technological innovation, regulatory oversight, and human expertise.
The lack of standardisation between the various rating agencies is one of the major challenges to the credibility of ESG criteria. Ratings are based on proprietary methodologies, which leads to variations and noise that can be very significant. MSCI1 takes an industry approach, Sustainalytics2 focuses on risk exposure, while Refinitiv3 focuses on reported quantitative data. Del Vitto et al. (2023) note that ESG rating agencies rely on distinct methodologies tailored to their own frameworks, which leads to significant variations in how the same company may be assessed across different providers, to such an extent that empirical studies show score variations of up to 50%. Differences in scope, indicator measurement and different weightings are the primary drivers of these discrepancies. Even though the CSRD in Europe seeks to harmonise the publication of ESG data, it does not impose a single rating methodology. This gap leaves room for AI-based solutions, which could help to align rating methodologies.
| Company | Business Sector | Refinitiv | MSCI | Sustainalytics |
| Tesla | Automotive | B (63/Good) | A (Average) | 28.6(Medium Risk) |
| Exxon Mobil | Energy | B (66/Good) | BBB (Average) | 36.5 (High Risk) |
Table 1.0 : Thomson Reuters, Refinitiv ESG score. ESG period: June 2022.
There are several AI-based initiatives to harmonise ESG ratings. SESAMm4 is one such solution. It specialises in analysing ESG controversies using AI, processing huge volumes of data from the media, reports and regulations, although this solution remains a hybrid approach in which ESG analysts validate the information generated. Adding to this, Clarity AI5, integrates ESG assessments directly into investment platforms, allowing asset managers to standardize their evaluations while meeting regulatory requirements. Beyond asset owners and managers, large corporations are also turning to AI tools such as C3 AI6, which helps transform ESG reporting from a mere compliance exercise into a strategic opportunity. As the adoption of these technologies accelerates, striking the right balance between automation and expert validation will be crucial to ensuring the reliability and credibility of ESG ratings.
It is also important to note that although AI is presented as a solution for standardising and improving the objectivity of ESG ratings, its effectiveness depends directly on the quality and diversity of the data on which it is trained. If the data used is biased, e.g. by favouring certain industries, excluding emerging markets, or only taking into account the voluntary declarations of companies, the resulting scores risk reinforcing existing asymmetries and misrepresenting actual sustainability performance.
In addition, the opaque nature of proprietary algorithms makes it difficult to understand ESG scores, demonstrating a real lack of transparency. This lack of transparency poses a major problem: how can we trust an ESG score if we don't know how it is calculated? To remedy these challenges, an open-source approach to ESG algorithms could offer an innovative and credible solution. Unlike proprietary models, open-source AIs allow full access to the code, evaluation criteria and decision-making mechanisms. This would allow investors, companies and regulators to audit the models, identify potential biases and improve the methodology in a collaborative manner.
Concrete examples show that open-source AI models can work while having a valid and relevant business model over time. For instance, Hugging Face offers an open-source AI platform where natural language processing (NLP) models are trained transparently, allowing the community to evaluate. Thus, rather than making the market more opaque, AI could become a tool for transparency and standardisation.
The introduction of AI and machine learning techniques represents a significant step toward enhancing the transparency and explainability of ESG ratings. One of the key contributions of AI in this domain is its ability to reverse-engineer rating methodologies. Using white-box and grey-box7 mathematical models, researchers have been able to replicate ESG scores with high accuracy, shedding light on which factors drive the ratings (Del Vitto et al., 2023). This approach enables a more structured understanding of how ESG scores are assigned, particularly by identifying the weight and influence of specific ESG metrics. A particularly powerful tool in this effort is the SHAP (SHapley Additive exPlanations) technique, which Del Vitto et al. (2023) describe as originating from cooperative game theory. This method estimates the contribution of each feature in an AI model, allowing users to understand why a company receives a certain ESG score. For example, if a rating is heavily influenced by carbon emissions data, SHAP analysis can highlight the precise impact of this variable relative to other ESG factors. However, despite its potential, AI does not eliminate all concerns regarding ESG rating opacity. Del Vitto et al. (2023) emphasize that even the most advanced machine learning models are unable to fully eliminate residual inconsistencies in ESG ratings, highlighting the inherent limitations of algorithmic approaches. This reinforces the necessity of maintaining human oversight to ensure accurate interpretation and contextual judgment.
As sustainability becomes a key concern in corporate strategy, greenwashing remains a significant risk. Investors and regulators face the challenge of distinguishing genuine sustainability commitments from misleading claims. A recent case illustrating this issue involves Delta Air Lines, which was sued in 2023 for allegedly misrepresenting itself as “the world’s first carbon-neutral airline.” The lawsuit contended that Delta's reliance on carbon offset programs; some of which were criticized as scientifically unverified or ineffective; failed to meaningfully counterbalance the airline’s actual emissions (Corder, M. 2024). This case highlights the difficulty stakeholders face in verifying the legitimacy of corporate sustainability narratives and the need for more robust verification mechanisms. In this context, artificial intelligence has emerged as a powerful tool for detecting ESG inconsistencies and verifying corporate sustainability reporting. AI enhances ESG reporting verification by automating the analysis of vast amounts of unstructured data. Mach (2023) describes how Covalence employs AI to analyze and categorize information from media sources, trade unions, and NGOs, enabling the assessment of a company’s ESG reputation and the consistency between its public commitments and actual practices, an approach particularly useful for identifying potential greenwashing. Another notable AI-driven tool in greenwashing detection is ClimateBERT, which uses NLP to assess the credibility of corporate climate commitments. Again, Mach (2023) explains that AI technologies can be applied to evaluate the credibility of sustainability statements made by corporate executives, particularly in financial communications such as earnings calls. This analytical capability is crucial, as sustainability claims are frequently vague or overstated, posing challenges for investors seeking to distinguish genuine commitments from strategic messaging.
AI plays a role in ESG taxonomy compliance. Certain algorithms are capable of estimating the proportion of revenues derived from specific business activities, facilitating the alignment of companies with the EU Green Taxonomy and other sustainability frameworks.
However, AI is not without its limitations and risks. As Mach (2023) warns, AI struggles with ambiguous language, sarcasm, and complex contexts in sustainability reports. "Polarity or sentiment analysis (positive vs. negative) is not easy in texts with ironic content," highlighting that while AI can flag potential inconsistencies, human oversight remains crucial to interpreting results accurately. Furthermore, generative AI models, if not properly monitored, may produce AI hallucinations8.
Despite the promise of artificial intelligence to bring structure and clarity to ESG analysis, its capacity to interpret ESG data remains significantly constrained by both technical limitations and conceptual ambiguities. These datasets are inherently heterogeneous, incomplete, and context-dependent. At the heart of the problem lies the semantic ambiguity of ESG language. As Mach (2023) points out, ESG analysis “is as much an art as a science”, and AI tools often falter when processing ironic, symbolic, or contextually rich language. This “art” in ESG analysis lies in the interpretive nature of sustainability data. Evaluating a company’s climate risk strategy or its labor rights record often involves qualitative judgment, contextual nuance, and sector-specific understanding. Two companies may disclose similar data, yet analysts may score them differently based on credibility, regional standards, or perceived intent. These grey zones demand human reasoning—something AI struggles to replicate. In contrast, the “science” involves quantifiable indicators and structured metrics. But without the art of interpretation, even the best algorithms risk missing the bigger picture. This is a crucial issue, given that ESG claims in reports and public communications often rely on narrative framing rather than measurable action. Another concern is data population and representativeness. Del Vitto et al. (2023) show that in the Environmental and Social dimensions, many variables are completely, or nearly empty in the datasets, and often filled with default values or substituted via correlation-based assumptions. These gaps reduce the reliability of model predictions and introduce systemic biases, especially for companies in emerging markets or industries with weaker disclosure norms.
AI enhances efficiency and replicability in ESG scoring, it struggles to interpret ESG data in a truly meaningful and context-aware way. The challenges are not merely computational, they are also epistemological, rooted in the complex, evolving, and normative character of sustainability itself. As Mach reminds us, AI should not replace human ESG expertise, but rather shift it toward algorithm supervision and result interpretation. In this light, AI should be seen not as an autonomous evaluator, but as a decision-support tool whose outputs require continuous human judgment and ethical oversight.
The increasing reliance on artificial intelligence in ESG scoring raises a paradox: while AI aims to support environmental goals, it can itself generate substantial environmental externalities, particularly through energy consumption. The question, therefore, is not only whether AI enhances the credibility of ESG ratings, but whether its ecological footprint undermines its legitimacy as a tool for sustainability. AI models, particularly large-scale language models, require significant computational resources. This energy demand is driven primarily by the training and deployment of models in vast data centers, many of which rely on non-renewable electricity sources. According to the Yale School of the Environment (2024), AI applications, including machine learning used in ESG, contribute to growing carbon emissions and water consumption due to cooling systems and hardware maintenance.
However, this impact is not uniform across geographies. In countries such as France, where the electricity mix is largely decarbonised thanks to nuclear and renewable sources, the environmental footprint of AI infrastructure is significantly lower. As a result, developing AI-based ESG rating systems in such regions could enhance their credibility, offering a more sustainable alternative to carbon-intensive models operated in fossil-fuel-dependent countries.
Once deployed at scale across financial institutions and ESG platforms, these systems add to the energy intensity of digital finance infrastructures. In this light, AI can become counterproductive, especially when used for superficial ESG assessments or unchecked greenwashing detection. To address these concerns, initiatives are emerging to quantify and reduce the carbon cost of AI. One example is the AI Energy Score, developed through a collaboration between Salesforce and Hugging Face, which aims to inform users of the energy impact of different AI models and promote more responsible development (ESG Today, 2024).
In conclusion, the use of AI in ESG scoring is justifiable only under certain conditions: its deployment must be energy-efficient, purpose-driven, and paired with transparent accountability mechanisms.
Artificial intelligence has demonstrated strong potential to improve the transparency, consistency and responsiveness of ESG ratings. From reverse-engineering opaque methodologies to identifying greenwashing risks and enhancing data verification, AI offers a powerful toolkit for addressing the current limitations of ESG scoring systems. Despite valid concerns, including algorithmic bias, data gaps and significant energy consumption, recent developments show that these risks can be mitigated through regulatory oversight, human supervision and the use of decarbonised infrastructures, such as those available in countries like France.
In this context, the use of AI in ESG scoring appears not only justified but also promising. If implemented with purpose, responsibility and transparency, AI can support a more credible and efficient ESG evaluation process. Far from replacing human judgment, it should act as a decision-support tool that strengthens rather than supplants sustainable finance practices.
[1] https://www.msci.com/documents/1296102/34424357/MSCI+ESG+Ratings+Methodology.pdf
[2] https://connect.sustainalytics.com/esg-risk-ratings-methodology
[5] https://clarity.ai/use-cases/
[6] https://c3.ai/products/enterprise-ai-for-sustainability/
[7] White-box models are methods where you can clearly see and understand how the results are calculated. Grey-box models are a mix—partly understandable, but with some elements that work like a “black box,” meaning their internal workings are less visible.
[8] AI hallucinations refer to instances where a model generates information that appears plausible but is factually incorrect or entirely fabricated. In ESG contexts, such errors can distort analysis and mislead decision-making.
[09] Nakhcha, M. et al. (2024) Synergy between Sustainable Finance and Artificial Intelligence: A Promising Future, International Journal of Applied Management and Economics, 2(07), pp. 104-117. DOI: 10.5281/zenodo.11102334.
[10] Berg, M. et al. (2023) ESG Ratings Explainability through Machine Learning Techniques, Sustainability, 15(21481). DOI: 10.3390/su15021481.
[11] Moodaley, W. et al. (2023) Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review, Sustainability, 15(1481). DOI: 10.3390/su15021481.
[12] Mach, A. et al. (2023) Artificial Intelligence and ESG Analysis, Allnews, 10 May. Available at: https://www.allnews.ch/content/points-de-vue/intelligence-artificielle-et-analyse-esg
[13] Yale School of the Environment (2024). Can we mitigate AI’s environmental impacts?. Available at: https://environment.yale.edu/news/article/can-we-mitigate-ais-environmental-impacts
[14] ESG Today (2024). Salesforce Partners Launch Energy Ratings for AI Models. Available at: https://www.esgtoday.com/salesforce-partners-launch-energy-ratings-for-ai-models
[15] Corder, M. (2024). An Amsterdam court has ruled KLM’s sustainable aviation advertising misled consumers. AP News. Available at: https://apnews.com/article/klm-greenwashing-aviation-sustainability-amsterdam-7d0e2b69099c7c9a46393a3cda44d4d8
[16] Hazel James Ilango (2022) Greater ESG Rating Consistency Could Encourage Sustainable Investments Available at: https://ieefa.org/sites/default/files/2022-10/Greater%20ESG%20Rating%20Consistency%20Could%20Encourage%20Sustainable%20Investments_final.pdf