In the age of AI-driven decision-making, ethical data is no longer optional — it’s the foundation of true sustainability.
Data has become the brain of the corporate world — and the sustainability agenda is no exception. ESG scores, carbon accounting, supply chain analyses, green finance criteria… all are built on massive datasets processed by algorithms.
But there’s a growing silent risk: data ethics.
Is the data fair? Accountable? Objective?
Because the systems processing the data are not as neutral as we assume. The datasets used to train algorithms are riddled with inherited biases.
As Cathy O’Neil aptly puts it: “Algorithms are opinions embedded in code — they automate the mistakes of the past.”
How reliable are ESG scores?
Today, nearly $40 trillion in global assets are managed according to ESG criteria. And yet, for the same company, ESG scores can differ by up to 50% depending on the data provider.
A study by MIT Sloan highlights this issue. There’s a crisis of standardization among ESG rating agencies. According to the London Stock Exchange’s 2024 report, 70% of investors find ESG data poor in quality and lacking comparability.
If we can’t trust the data driving such critical financial decisions, we’re facing a serious problem. ESG loses its role as a “sustainability anchor” and instead becomes a structure suffering from a crisis of legitimacy.
That’s a key reason behind the current backlash against ESG in the corporate world. The system’s lack of legitimacy is weak.
Sustainability is, at its core, legitimacy management
Sustainability is not just about being environmentally friendly. It’s about maintaining legitimacy — in the eyes of financial markets and the public. Companies showcase their sustainability performance to show they’re on the “right path.”
But if the systems measuring that performance lack transparency, we enter an era of ethical erosion.
That’s why the data behind sustainability metrics is not just technical — it’s political, cultural, and ethical.
Data isn’t just collected — it’s constructed
As sociologist Bruno Latour reminds us,
“Data is not a passive resource — it is an active product of social relations.”
Sustainability is shaped by who gets to produce the data, whose story gets visibility, and who is left out entirely. In this way, data becomes a new instrument of power behind investment decisions, regulatory policies, and corporate narratives.
The key fracture is this:
Data must be fair. Accountability requires transparency. Models should not operate like black boxes. Otherwise, these systems — under the guise of sustainability — may begin to generate new asymmetries.
That’s how we step into a post-truth era of sustainability.
In a post-truth world, reality is built around perception and interest-driven narratives. Data exists — but transparency around how it’s produced, presented, and interpreted steadily declines.
In this environment:
- Uncertainty becomes the new corporate norm.
- Green-washing becomes systematic.
- Sustainability performance becomes manipulable.
- And ultimately, the entire concept of sustainability collapses.
So, what’s the way out?
For the corporate world, here are some key building blocks:
- Data Ethics Boards
Multidisciplinary and independent oversight mechanisms should be established. All models must undergo regular bias, equity, and ethics testing. - Transparent Governance Models
What data feeds the algorithms, and how do they construct models? The framework must be transparent, auditable, and accountable. Decision-making processes must be open to scrutiny. - Comprehensive Education Programs
Ethical data use and algorithmic awareness should be included in corporate training. This applies not just to tech teams, but across all departments.
Conclusion: A New Arena of Legitimacy
Let’s admit it: Sustainability has long been treated as a technical issue, largely reduced to carbon emissions. In the age of AI, the focus must shift. We need to prioritize the justice of information systems. It is essential to ensure the transparency of data processes. We must also consider the legitimacy of decision-making mechanisms.
Because algorithms don’t just calculate — they make normative decisions. They encode values, shape outcomes, and influence the distribution of economic resources, social opportunities, and even political power.
In the age of AI, data ethics is not just a component of good governance. It is the prerequisite for any meaningful sustainability strategy.
When this is neglected, sustainability becomes nothing more than a shiny wrapper for empty scores and performative reputation campaigns.
At the end of the day, technology does its job. But without a strong institutional culture, there is no guarantee of ethical or accurate performance.
Where that is lacking, the promise of sustainability becomes nothing more than an ornamental green façade.