Enterprise AI Analysis
Artificial intelligence in environmental and Earth system sciences: explainability and trustworthiness
Explainable Artificial Intelligence (XAI) methods are gaining traction in environmental and Earth system science to enhance decision-making. This review highlights the rapid expansion of XAI applications, especially SHAP/Shapley values, across diverse domains like ecology, geology, and remote sensing. Despite the growing use of XAI to improve model understanding and prediction reliability, a critical gap exists in systematically addressing and enhancing trust in AI applications, with only 1.2% of studies explicitly focusing on trustworthiness as a core objective. The review emphasizes the need for a "human-centered" XAI framework involving multiple stakeholders to build truly trustworthy AI systems for complex environmental problems.
Executive Impact
Transforming Environmental Science with Trustworthy AI
Our analysis reveals key trends and critical opportunities for leveraging XAI to drive impactful, transparent, and reliable AI solutions in environmental and Earth system sciences.
0%
Annual Growth Rate of XAI
0%
Studies Addressing Trust
0 Articles
Most Popular XAI Method (SHAP/Shapley)
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Key Application Domains
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Rapid Expansion of XAI in Environmental Sciences
The annual growth rate for XAI publications in environmental and Earth system science is 90%, indicating a rapid and significant expansion of its application over the past decade. This widespread adoption underscores the increasing recognition of XAI's value in these critical domains.
90% Annual Growth Rate of XAI PublicationsEnterprise Process Flow
Documents identified via keyword search (n=595)
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Filtering XAI methods independently (n=17 searches)
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Filtering for trust* (n=39)
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Selection of time span: 2015-2023 (n=575)
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Removal of synonyms/initial keywords
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Identification of major publication trends (n=575)
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Investigation of XAI method popularity
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Assessing trust role in environmental sciences
| Method | Key Advantages | Common Use Case |
|---|---|---|
| SHAP/Shapley Values |
|
Feature importance, dependence, interaction explanation |
| LIME |
|
Explaining individual predictions |
| Partial Dependence Plots (PDP) |
|
Global interpretation of feature-output relationship (assumes independence) |
The Trust Gap in XAI Research
Despite frequent claims that XAI enhances trust, only a minimal percentage (1.2%) of studies in environmental and Earth system sciences explicitly address trustworthiness as a core research objective, highlighting a critical knowledge gap.
1.2% Studies with Trust as Core ObjectiveUser-Centered XAI for Trustworthy Decision-Making
“Co-developing the AI life cycle together with multiple viewpoints may take time but can be the most effective way to facilitate trustworthy AI systems for tackling complex environmental problems.”
Source: Bostrom et al. 2024
Context: The importance of interdisciplinary collaboration and user-centered design in building trust in AI.
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Your Path to Trustworthy AI
Proposed Implementation Roadmap
A structured approach to integrate explainable and trustworthy AI into your environmental and Earth system science operations.
Phase 1: Needs Assessment & Data Audit
Identify high-stakes decision points, stakeholder needs for explainability, and conduct a thorough audit of existing environmental datasets for AI readiness.
Phase 2: XAI Model Prototyping & Integration
Develop initial AI models incorporating XAI techniques (e.g., SHAP, LIME) tailored for spatial/temporal data, integrating feedback loops for iterative refinement.
Phase 3: Trustworthiness Validation & User Feedback
Empirically assess the impact of XAI explanations on user trust and understanding, using social science methods (surveys, interviews) with diverse stakeholder groups.
Phase 4: Human-Centered AI Framework Development
Design and implement a comprehensive framework that integrates XAI, ethical principles, and user-centered design across the entire AI lifecycle.
Phase 5: Continuous Monitoring & Education
Establish mechanisms for ongoing performance and trustworthiness monitoring, coupled with educational programs for AI developers and end-users.
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