From speed to trust: ensuring transparency in AI-based extreme weather forecasts

Area of Science:

• Meteorology
• Computer Science
• Climate Science

Background:

• Artificial intelligence (AI) offers faster, cheaper extreme-weather predictions than traditional models.
• AI in forecasting presents challenges in transparency, impacting trust, equity, and resilience.
• Current AI early warning systems may lack the necessary clarity for high-stakes applications.

Purpose of the Study:

• To examine transparency in AI-based weather forecasting.
• To assess AI's impact on predictive integrity, societal fairness, and long-term resilience.
• To argue that accuracy alone is insufficient for AI in critical forecasting contexts.

Main Methods:

• Analysis of transparency in AI forecasting across three dimensions: predictive integrity, societal fairness, and long-term resilience.
• Review of recent regulatory developments in meteorological practice.
• Identification of practical measures for enhancing AI forecast transparency.

Main Results:

• AI-driven forecasts are faster and more computationally efficient.
• Lack of transparency in AI decision-making poses risks to trust and equity.
• Accuracy is not the sole metric for evaluating AI in high-stakes forecasting.

Conclusions:

• Transparency is crucial for trustworthy and equitable AI-driven early warning systems.
• Practical measures like harmonized labeling and model cards are proposed.
• Integration into international frameworks is essential for effective AI in meteorology.