This risk is often described as the “black box problem.” While the term is widely used, its implications are still underestimated—particularly in the context of regulatory scrutiny, assurance, and long-term decision-making.
Opacity as a structural challenge in AI-enabled systems
At its core, the black box problem refers to the difficulty of explaining how advanced computational systems arrive at a specific output. Data is ingested, processed, and transformed, yet the internal logic—how variables interact, how trade-offs are resolved, and why certain outcomes emerge—remains largely inaccessible to human interpretation.
This opacity is not accidental. Many AI and machine learning models are designed to prioritise predictive performance over interpretability. Highly non-linear mathematical relationships, layered architectures, and dynamic feature weighting allow systems to handle vast and heterogeneous datasets, but they do so at the expense of clear causal explanations. Even when individual components are technically documented, the combined behaviour of the system often defies straightforward analysis.
In domains where decisions are low-risk or reversible, this may be acceptable. In sustainability and ESG contexts, it is not.
Why transparency matters in sustainability decision-making
Sustainability data increasingly informs decisions with material consequences: capital allocation, product portfolio strategy, supplier inclusion, regulatory compliance, and public disclosure. As frameworks such as CSRD, ISSB/IFRS Sustainability Standards, and various due diligence regulations mature, expectations around traceability, methodological clarity, and accountability are tightening.
When automated systems produce environmental indicators, risk scores, or scenario analyses without a clearly auditable decision path, several problems emerge simultaneously. Verification becomes more difficult, not because the data is necessarily incorrect, but because its provenance cannot be convincingly demonstrated. Internal confidence erodes, as experts are asked to stand behind results they did not fully construct or interrogate. Externally, trust weakens—among auditors, regulators, investors, and stakeholders—precisely at a time when credibility is paramount.
This tension is already visible in areas such as climate scenario modelling, supply chain due diligence, and ESG ratings. Financial institutions and corporates alike rely on proprietary algorithms whose classifications can materially affect investment decisions, yet the underlying drivers of those classifications are often opaque. Academic research has begun to highlight this gap, noting that while AI adoption across ESG rating providers is both widespread and accelerating, transparency and bias remain unresolved concerns.
From formal compliance to substantive due diligence
The black box problem also intersects with a broader issue in sustainability governance: the difference between formal compliance and meaningful due diligence.
A system that produces compliant outputs without exposing its underlying logic risks encouraging a “check-the-box” dynamic. Targets may be met on paper, reports may be generated on time, and disclosures may align with formal requirements—yet the organisation remains poorly equipped to understand the actual drivers of impact or risk.
Effective sustainability management, by contrast, requires continuity, context, and learning over time. Short-term ESG objectives must be quantitatively meaningful and clearly connected to operational change, rather than achievable through business-as-usual adjustments. Due diligence must be ongoing and risk-based, capable of identifying root causes, monitoring progress, and supporting remediation where needed. None of this is possible if the systems involved cannot explain their own conclusions.
Opacity, in this sense, is not merely a technical limitation. It becomes a governance issue.
Designing systems that support accountability, not abstraction
Avoiding the black box problem does not mean rejecting automation or AI. On the contrary, given the scale and complexity of today’s sustainability requirements, intelligent automation is indispensable. The question is how it is implemented.
Systems designed for sustainability work must treat transparency as a foundational requirement rather than an optional feature. This includes clear visibility into data sources, explicit handling of assumptions, consistent use of verified background datasets, and the ability to trace reported figures back to underlying inputs. Automation should reduce manual effort and repetition, but it should not obscure methodological choices or remove expert oversight.
This is where the distinction between generic AI tools and domain-specific sustainability platforms becomes critical. In sustainability, explainability is not a “nice to have”; it is a prerequisite for trust, assurance, and informed decision-making.
A different approach to AI-assisted sustainability work
EandoX CoPilot has been developed with these constraints explicitly in mind. Rather than functioning as a decision-making black box, it operates as an assistive layer within a structured, standards-based sustainability workflow.
AI is applied where it adds the most value—such as interpreting and structuring complex supplier documentation—while keeping data, assumptions, and calculations fully accessible. Sustainability data is organised within a unified product data library that supports reuse across EPDs, LCAs, CSRD reporting, and related requirements. Because this data foundation is shared, updates propagate consistently, reducing the risk of divergence between reports while maintaining full traceability.
Crucially, results generated within the system can always be examined, validated, and explained. This design choice reflects a simple premise: automation should strengthen professional judgement, not replace it.
Towards explainable, resilient sustainability systems
As sustainability reporting and decision-making continue to scale, the industry faces a choice. It can accept increasingly opaque systems in exchange for speed, or it can insist on tools that combine efficiency with accountability.
The direction of regulation, assurance practice, and stakeholder expectations suggests that the latter will prevail. Explainable AI, transparent data architectures, and verifiable decision paths are no longer theoretical ideals; they are becoming practical necessities for organisations that take sustainability seriously.
For those interested in exploring how AI can be integrated into LCA and sustainability work without sacrificing methodological integrity, EandoX offers a dedicated training session: Transforming LCA with AI. The focus is not on automation for its own sake, but on how emerging technologies can be applied responsibly—supporting scale, consistency, and insight while preserving clarity and control.
In sustainability, credibility is cumulative. The systems we choose today will shape not only what we report, but how confidently we can stand behind it in the years ahead.
