I Have No Mouth But I Must Scream

By Lugine Gray

Since the advent of AI, one thing is clear: it isn’t going anywhere. Like electricity or the internet, AI is becoming part of how we see risk, plan cities, and steward ecosystems. The real question is whether it will evolve to help nature—or hurt it. Used well, AI helps us see ecosystems more clearly, predict hazards earlier, and move credible nature-based solutions (NBS) from research to construction. Used poorly, it can misdirect funding, deepen inequities, and turn fieldwork into dashboards with no follow-through. The goal isn’t AI or no AI—it’s using AI as a power tool for resilience while keeping people, ecosystems, and maintenance at the center.

When AI helps, it does so by turning messy data into timely, practical decisions. Along coastlines, models fed by satellite and drone imagery can map mangrove loss, simulate storm-surge attenuation, and pinpoint the stretches of shoreline where reef or marsh restoration will most reduce flood depth. After a hurricane, those same pipelines can assess damage in hours instead of weeks, guiding debris removal and temporary protections to the most critical facilities first. In overheated cities, computer vision and thermal imagery can identify micro-hotspots—bus stops, daycare routes, warehouse districts—and recommend tree canopy corridors and permeable surfaces that simultaneously cut peak temperatures and reduce flash flooding. In watersheds, machine learning can forecast debris flows and place bioswales, rain gardens, and culvert upgrades where each dollar prevents the greatest downstream losses. For biodiversity and restoration finance, acoustic and camera-trap AI can track species richness and canopy regrowth, producing credible measurement-reporting-verification (MRV) that unlocks performance-based funding without waiting on slow manual surveys.

The promise is real when it translates to builds. In one Gulf Coast parish, planners blended lidar, shoreline change, and surge models with an AI ranking tool to prioritize 20 kilometers of oyster-reef and marsh restoration. The output didn’t stop at pretty maps—it identified segments that most reduced expected surge at hospitals and lift stations, accounting for neighborhoods with low mobility. That ranking became a build sheet tied to operations and maintenance, with sensors set to trigger work orders when thresholds were crossed. In a major U.S. city, an AI heat-risk project fused thermal imagery, 311 complaints, and parcel data to design a network of shade corridors and permeable alleys. Community co-design adjusted species mixes and added shade near daycare paths; those tweaks were codified into design standards so the benefits would persist beyond a pilot.

When AI harms, it’s usually because the inputs are biased, the objective is too narrow, or the tools are allowed to replace judgment. “Garbage in, garbage out” isn’t a cliché here: rural, low-income, or Indigenous lands are often under-mapped, so models trained on incomplete data steer investments away from places that need them most. Optimization can ignore people—an algorithm might maximize benefit-cost ratios while underserving vulnerable neighborhoods or smallholders. Over-automation is another trap: “the model said it’s safe” is not due diligence when gauges fail, sensors drift, or land use changes. And we can’t ignore energy use—training and serving large models carries a carbon cost that must be managed with efficient architectures, low-carbon hosting, and right-sized compute.

Two hard-lesson examples make this plain.

A regional flood-mitigation program used AI to rank green-infrastructure sites but relied on parcel data that undercounted renters and informal businesses in industrial corridors. The model sent money toward already-leafy neighborhoods with solid data coverage, while flood-exposed workers near warehouses saw little benefit. Only after an equity audit and community ground-truthing did the program revise its objective function with equity weights and rebalance the portfolio. Elsewhere, a restoration project touted AI-verified carbon gains to unlock financing, but its MRV pipeline was opaque and skipped independent spot checks. When drought hit, tree mortality spiked and the promised benefits didn’t materialize, eroding trust and freezing future funding. Both cases underline the need for transparent data, documented model limits, and third-party validation.

Best-case scenario (AI helping nature): Cities, utilities, and coastal parishes adopt a shared “see → prioritize → deploy → steward” pipeline. Baselines are built from satellites, drones, sensors, and community science with clear data specs and QA/QC. Portfolio tools estimate risk reduction per dollar while applying equity and livelihood constraints, so projects protect hospitals, lift stations, schools, and high-exposure workers first. Permitting is accelerated with AI assistants that surface relevant precedents, while funding packages (grants, bonds, public-private) are assembled with transparent costs, uncertainty, and monitoring. Once built, sensors trigger O&M tasks, annual model retraining captures land-use change and climate drift, and neighborhood-level outcomes are openly reported. The net effect: fewer disaster losses, cooler streets, cleaner water, stronger habitats—and durable public trust because the system is legible and fair.

Worst-case scenario (AI hurting nature): Leaders chase optimization without governance. Under-mapped communities are treated as low priority because “the data says so.” Dashboards become a substitute for field verification; MRV is proprietary and unreviewed; carbon and resilience benefits are over-claimed to unlock financing. Projects cluster where data is plentiful, not where risk is highest. Maintenance budgets aren’t tied to model triggers, so assets decay while the dashboard still looks green. A few high-profile failures (dead plantings, missed floods, equity blind spots) trigger public backlash; regulators clamp down; financing dries up; and communities become rightly skeptical of any data-driven plan—good or bad.

So how do we keep AI honest? Start with a decision, not a dataset: what policy, permit, capital plan, or maintenance action will change if the model says X? Establish baselines and outcomes first—losses avoided, extreme-heat days reduced, habitat restored—and audit the data for coverage and bias. Keep a model card for every model, publish distributional results by neighborhood, and include equity and livelihood constraints so solutions don’t maximize ROI at someone else’s expense. Tie outputs to funding gates, permits, and O&M work orders so the work doesn’t stop at a pretty map. Track uncertainty and explain it to decision-makers. Right-size compute, run in low-carbon regions, and measure your AI energy use. Finally, keep humans in the loop: name the decision owner who can approve or override, run tabletop “what if it’s wrong?” exercises, and maintain an incident playbook for when to pause or retrain.

A pragmatic way to operationalize this is simple: see → prioritize → deploy → steward. First, see: blend satellite, drone, and field sensors to build baselines for canopy, wetlands, erosion, flood depth, and heat with a clear data spec and QA/QC. Next, prioritize: estimate loss reduction for each NBS option and add constraints for equity zones, critical infrastructure, and cultural sites; rank by risk reduction per dollar and co-benefits like heat relief, biodiversity, and water quality. Then, deploy: auto-extract permitting requirements and precedents, prepare investment memos with costs, benefits, uncertainty, and monitoring, and align with grants, bonds, or public-private finance. Finally, steward: monitor outcomes with sensors and community science, trigger maintenance tasks when thresholds are crossed, retrain annually with new data, and publish short “what changed, why” updates.

AI is here to stay—and ideally it will accelerate the labor-intensive work (data wrangling, comparisons, compliance) so we can spend more time outdoors with residents, walking sites, and building projects that last. When we prioritize people, ecosystems, and maintenance, AI becomes what it should be: a powerful tool for resilience, not a substitute for judgment. Used well, it helps us anticipate sooner, decide smarter, build faster, and steward longer; used carelessly, it becomes an adversary for nature’s remaining water—an engine that drinks before the roots.