Evaluate diversity in data sets to improve fairness
Explore and filter model evaluation results
Display competition information and manage submissions
This is AI app that help to chat with your CSV & Excel.
A Leaderboard that demonstrates LMM reasoning capabilities
Form for reporting the energy consumption of AI models.
Submit evaluations for speaker tagging and view leaderboard
Search for tagged characters in Animagine datasets
Life System and Habit Tracker
Analyze weekly and daily trader performance in Olas Predict
Predict linear relationships between numbers
Browse and filter AI model evaluation results
Detect bank fraud without revealing personal data
Measuring-diversity is a tool designed to evaluate diversity in data sets with the goal of improving fairness and reducing bias. It provides insights into how well-represented different groups are within a dataset, helping users identify disparities and take corrective actions.
• Comprehensive analysis: Assess diversity across multiple dimensions such as gender, race, age, and more.
• Bias detection: Identify underrepresented or overrepresented groups in your data.
• Visualization tools: Generate charts and graphs to clearly illustrate diversity metrics.
• Customizable thresholds: Set benchmarks for fairness and receive alerts when thresholds are not met.
• Integration: Easily incorporate into existing data workflows and pipelines.
pip install measuring-diversity).What types of data can measuring-diversity analyze?
Measuring-diversity can analyze any structured dataset, including CSV files, databases, and DataFrames. It is particularly effective for datasets with demographic information.
How does measuring-diversity detect bias?
The tool compares the representation of different groups in your dataset to predefined fairness thresholds. If a group falls below the threshold, it is flagged as underrepresented.
Can I customize the fairness thresholds?
Yes, measuring-diversity allows users to set custom thresholds based on their specific needs or industry standards. This ensures tailored fairness evaluations.