Sustainability & AI
Explainable AI (XAI)
Techniques making AI model outputs understandable to humans, supporting accountability for AI-influenced sustainability decisions.
Definition
Techniques and design approaches that make the behaviour and outputs of AI systems understandable to humans, enabling inspection, debugging and accountability.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
- Also known as
- XAI · interpretable AI
References
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This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
Explainability methods range from inherently interpretable models to post-hoc explanations of black-box systems (feature attribution, counterfactuals, example-based explanation). Miller's 2019 synthesis grounded the field in how humans actually explain decisions; the EU's trustworthy-AI framework lists explicability among its requirements.
How it is used
Practitioners apply XAI where reasons matter: credit and risk scoring, screening decisions, scientific use of models, and regulatory contexts requiring 'meaningful information about the logic involved'. Environmental applications include validating that a model uses physically meaningful signals.
Why it matters
An explanation is what turns an output into a decision that can be defended, contested and improved. Where AI touches sustainability compliance or justice, explainability is the difference between a tool and an oracle.
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