ypesk/frugal-ai-EURECOM-Submission-2

Random Baseline Model for Climate Disinformation Classification

Model Description

This is a random baseline model for the Frugal AI Challenge 2024, specifically for the text classification task of identifying climate disinformation. The model serves as a performance floor, randomly assigning labels to text inputs without any learning.

Intended Use

• —Primary intended uses: Baseline comparison for climate disinformation classification models
• —Primary intended users: Researchers and developers participating in the Frugal AI Challenge
• —Out-of-scope use cases: Not intended for production use or real-world classification tasks

Training Data

The model uses the QuotaClimat/frugalaichallenge-text-train dataset:

• —Size: ~6000 examples
• —Split: 80% train, 20% test
• —8 categories of climate disinformation claims

Labels

1. 1.No relevant claim detected
2. 2.Global warming is not happening
3. 3.Not caused by humans
4. 4.Not bad or beneficial
5. 5.Solutions harmful/unnecessary
6. 6.Science is unreliable
7. 7.Proponents are biased
8. 8.Fossil fuels are needed

Performance

Metrics

• —Accuracy: ~12.5% (random chance with 8 classes)
• —Environmental Impact:
• —Emissions tracked in gCO2eq
• —Energy consumption tracked in Wh

Model Architecture

The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline.

Environmental Impact

Environmental impact is tracked using CodeCarbon, measuring:

• —Carbon emissions during inference
• —Energy consumption during inference

This tracking helps establish a baseline for the environmental impact of model deployment and inference.

Limitations

• —Makes completely random predictions
• —No learning or pattern recognition
• —No consideration of input text
• —Serves only as a baseline reference
• —Not suitable for any real-world applications

Ethical Considerations

• —Dataset contains sensitive topics related to climate disinformation
• —Model makes random predictions and should not be used for actual classification
• —Environmental impact is tracked to promote awareness of AI's carbon footprint