Risks of denaturalization and loss of the human perspective in environmental decision-making
“AI can help us see further, but it must not replace the human gaze nor the direct relationship with living systems”
Artificial Intelligence (AI) has become established over the past decade as one of the most disruptive technologies across virtually all fields of human knowledge. Its ability to process large volumes of data, integrate heterogeneous information, identify complex patterns, and generate predictions has sparked enormous interest in sectors traditionally intensive in information, such as natural resource management, spatial planning, agriculture, hydrology, and ecosystem conservation. In a context marked by climate change, accelerated environmental degradation, and increasing anthropogenic pressure on natural systems, AI is often presented as an almost inevitable tool to improve efficiency, optimise resource use, and support decision-making.
This growing prominence of AI is reinforced by the development of remote sensing technologies, environmental monitoring networks, geographic information systems, and big data platforms, which generate unprecedented volumes of information on the state and dynamics of ecosystems. In this scenario, machine learning algorithms appear to offer an effective response to the increasing complexity of environmental management, enabling the anticipation of scenarios, the detection of anomalies, and the assessment of impacts with a speed and scale previously unimaginable.
However, this technological enthusiasm also entails profound risks that are rarely addressed with the necessary critical attention. Natural resource management is not merely a technical or computational problem; it is, above all, an ecological, social, cultural, and ethical process that requires a deep understanding of ecosystems, their non-linear dynamics, and the historical relationship between human communities and their environment. Reducing this complexity to a set of variables optimised through algorithms may lead to a simplified and impoverished vision of sustainability.
In this sense, a poorly critical adoption of AI may lead to a progressive de-naturalisation of environmental management, displacing direct experience, local knowledge, and ecological intuition with algorithmic models that, while seemingly precise, may be conceptually reductive. The risk does not lie in the technology itself, but in the tendency to grant it a central and decisive role in contexts where uncertainty, complexity, and human values are fundamental elements.
The complexity of natural systems: what AI cannot simplify
Ecosystems are complex adaptive systems, characterised by multiple interactions between biotic and abiotic components, non-linear feedbacks, critical thresholds, and emergent processes that are difficult to predict. Unlike purely technical systems, natural systems do not respond proportionally to disturbances, nor can they be fully described through deterministic models or simple causal relationships.
This complexity implies that small alterations may generate disproportionate effects, and that systems may experience abrupt ecological regime shifts when certain thresholds are exceeded. Resilience, adaptive capacity, and self-organisation are key properties of ecosystems that depend on dynamic interactions that are difficult to capture using static models or approaches based exclusively on historical data.
AI, by definition, is based on the identification of patterns from large volumes of observed data. While this approach allows useful predictions in well-characterised contexts, it presents a structural limitation when facing poorly observed ecological processes, extreme events, or rapidly changing scenarios such as those induced by climate change. There is a risk that AI models may confuse correlation with causation or extrapolate past trends into contexts where environmental conditions are changing radically.
Moreover, many key ecological processes—such as soil–microbiota–plant interactions, ecosystem resilience mechanisms, or species’ adaptive responses—are not fully represented in the available datasets. Excessive reliance on AI may generate a false sense of control and understanding, while in reality excessively simplifying the inherent complexity of natural systems.
Loss of direct experience and ecological knowledge
One of the most concerning effects of the intensive use of AI in environmental management is the progressive detachment between managers and the territory. Traditionally, natural resource management has been based on direct observation, fieldwork, continuous interaction with the environment, and cumulative learning over time. This experience generates tacit, deeply contextual knowledge that is difficult—if not impossible—to encode into algorithms.
When decisions are made primarily through dashboards, predictive models, and automated recommendations, there is a risk that professionals lose contact with the biophysical reality of the systems they manage. Nature ceases to be perceived as a living, dynamic, and heterogeneous system and becomes a digital abstraction composed of data layers, synthetic indices, and thematic maps.
This process not only impoverishes ecological understanding but may also affect the training of new generations of technicians, scientists, and managers, who could develop excessive dependence on automated tools without acquiring critical thinking or deep ecological sensitivity. In the long term, this loss of direct experience may weaken adaptive capacity and responsiveness to unforeseen situations or environmental crises.
AI is fundamentally nourished by structured, quantifiable, and standardised data. However, an essential part of the knowledge associated with natural resources resides in local and traditional knowledge systems, built over generations through direct interaction with the environment. This knowledge includes adaptive agricultural practices, traditional water management strategies, landscape interpretation, and the understanding of subtle ecological signals that cannot always be translated into formal data.
The risk is that AI, by being unable to easily incorporate this non-formalised knowledge, contributes to its invisibilisation and, ultimately, to its progressive loss. Decisions based exclusively on algorithmic models may conflict with locally sustainable practices, generating social rejection, loss of legitimacy, or even negative environmental impacts resulting from the disruption of consolidated socio-ecological balances.
From an ethical and social perspective, relegating local knowledge in favour of technocratic solutions may deepen inequalities, weaken participatory governance of natural resources, and erode shared responsibility for their management.
AI tends to optimise specific variables: productivity, water-use efficiency, yield, cost reduction, or the maximisation of certain environmental indicators. However, sustainable natural resource management necessarily involves the consideration of multiple objectives and trade-offs, some of which are difficult to quantify, such as the cultural value of landscapes, functional biodiversity, social equity, or human well-being.
There is a danger that AI systems promote a reductionist vision of sustainability, focused on easily measurable indicators while ignoring essential qualitative dimensions. This approach may lead to decisions that appear optimal from an algorithmic perspective but are ecologically poor, socially unacceptable, or even counterproductive in the long term.
Another critical aspect is the perception of AI as a neutral and objective tool. In reality, algorithms reflect the decisions, values, and assumptions of those who design them, as well as the biases present in training data. In the environmental field, this may translate into models that implicitly prioritise certain land uses, analytical scales, or specific interests.
Delegating complex decisions to AI systems without a clear ethical framework and effective human oversight may erode responsibility and dilute accountability. In natural resource management, where decisions have long-term and often irreversible impacts, this loss of responsibility is particularly concerning.
Natural resource management is not only a matter of technical optimisation, but also of values, priorities, and visions of the future. Deciding how to manage an aquifer, a forest, or an agricultural system involves ecological, social, cultural, and ethical considerations that cannot be fully automated.
The human perspective provides essential elements: the ability to interpret complex contexts, integrate interdisciplinary knowledge, anticipate social conflicts, and make decisions under conditions of deep uncertainty. Empathy, prudence, and intergenerational responsibility are intrinsically human dimensions that cannot be replaced by algorithms, regardless of how advanced they may be.
Recognising the risks associated with AI does not imply rejecting its use. On the contrary, AI can be an extremely valuable tool to support natural resource management if used critically, complementarily, and contextually. The real challenge lies in preventing technology from displacing the ecological and human essence of environmental management.
In this sense, a balanced approach will involve:
• Using AI as a support tool, not as a substitute for human judgement.
• Integrating quantitative data with local knowledge and field-based experience.
• Maintaining participatory and transparent decision-making processes.
• Training professionals in critical thinking, not only in technical competencies.
• Explicitly recognising the limitations, assumptions, and biases of algorithmic models.
Artificial Intelligence offers undeniable opportunities to improve natural resource management, but it also poses significant risks if adopted uncritically. Nature is not a purely optimisable system, nor can sustainability be reduced to a computational problem.a. La naturaleza no es un sistema puramente optimizable, ni la sostenibilidad puede reducirse a un problema de cálculo.
Losing naturalness in environmental management means losing the ability to listen to the territory, understand its signals, and act prudently in the face of uncertainty. AI can help us see further, but it must not replace the human gaze nor the direct relationship with living systems.
In the context of a global environmental crisis, preserving this human and ecological perspective is not a luxury, but an essential condition for truly sustainable natural resource management.
Forest Engineer and Master´s degree in Research in Agroforestry Development Engineering. He works to apply this expertise in the development of Nature Based Solutions in cities (green roofs, smarts soils, urban farms, green corridors, improvement of the landscape connectivity, actions against erosion and green well-being areas).
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