Synthetic Data for Responsible AI and Automation Decisions

Synthetic dataset designed for research on responsible AI and automation decision systems. The dataset is tagged for intermediate-level users and focuses on topics like AutoML and artificial intelligence. Specific details on size, structure, and authorship are not provided.

Use Cases

• Train models on synthetic data for responsible AI system evaluation, leveraging the 'Automation decision systems' focus.
• Benchmark AutoML (Automl) pipelines using a dataset specifically tagged for intermediate complexity.
• Analyze patterns in synthetic data generation for the 'Artificial Intelligence' and 'Synthetic' tags to understand bias mitigation techniques.

Strengths

• Dataset is explicitly designed for the topical focus of responsible AI and automation decisions.
• Tagged as 'Intermediate' complexity, indicating a target user skill level.
• Includes relevant topical tags such as 'Automl', 'Artificial Intelligence', and 'Synthetic'.

Limitations

• All structural details are unknown, including row count, column names, and file formats.
• The synthetic nature of the data may limit direct applicability to real-world scenarios without validation.
• Lacks provenance information such as author, organization, license, and update history.

Provenance

Source

Kaggle

Collection Method

Synthetic generation