Causality for Trustworthy AI: Curated Research and Software Resources

Maintained by the L3S Research Center and last updated in September 2023, this GitHub repository provides a curated collection of resources for causality and trustworthy AI. It categorizes tools, datasets, and software packages across specialized domains including healthcare, drug discovery, and federated learning.

Use Cases

• Benchmarking causal discovery algorithms using the 'Datasets' category
• Implementing bias mitigation techniques in 'healthcare' models using linked tools
• Evaluating model 'robustness' and 'adversarial-machine-learning' defenses via curated packages

Strengths

• Categorizes resources across 20 specific tags including OOD and federated learning
• Maintained by L3S, a specialized research center for web science
• Includes domain-specific sections for healthcare and drug discovery

Limitations

• Not a standalone dataset but a collection of external links
• No standardized format or schema for the referenced datasets
• Lack of quantitative metrics for the size or scale of the linked resources

Provenance

Source

L3S Research Center

Collection Method

Curated

Time Range

Up to September 2023

Freshness

Last updated September 12, 2023.

Geography

Global