| Name | Version | Summary | date |
| fairo |
25.7.1 |
SDK for interfacing with Fairo SaaS platform. |
2025-07-18 02:43:24 |
| mdca |
0.1.16 |
MDCA: Multi-dimensional Data Combination Analysis. It's used to analysis data table through multi-dimensional data combinations. Multi-dimensional distribution, fairness, and model error analysis are supported. |
2025-03-15 04:31:51 |
| langtest |
2.6.0 |
John Snow Labs provides a library for delivering safe & effective NLP models. |
2025-03-11 05:25:18 |
| bias-lens |
0.0.2 |
A library for bias detection and explainable AI methods in NLP models. |
2025-02-23 08:16:06 |
| daindex |
0.6.4 |
Deterioration Allocation Index Framework |
2025-02-09 11:15:26 |
| oracle-automlx |
25.1.1 |
Automated Machine Learning with Explainability |
2025-02-03 19:58:35 |
| langfair |
0.3.2 |
LangFair is a Python library for conducting use-case level LLM bias and fairness assessments |
2025-01-15 17:25:26 |
| DivExplorer |
0.2.6 |
Analyze Pandas dataframes, and other tabular data (csv), to find subgroups of data with properties that diverge from those of the overall dataset |
2024-12-06 21:07:30 |
| oracle-guardian-ai |
1.2.0 |
Oracle Guardian AI Open Source Project |
2024-11-13 01:59:38 |
| data-generation-tool |
1.1.1 |
A library that provides data generation functionality for AI and data science projects |
2024-11-06 15:57:30 |
| xaiographs |
1.2.0 |
Python library providing Explainability & Fairness AI functionalities |
2024-10-09 10:32:34 |
| nhssynth |
0.10.2 |
Synthetic data generation pipeline leveraging a Differentially Private Variational Auto Encoder assessed using a variety of metrics |
2024-10-04 11:54:37 |
| DAindex |
0.1.0 |
Deterioration Allocation Index Framework |
2024-09-12 17:06:46 |
| thetis |
0.2.1 |
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects. |
2024-06-11 14:26:21 |
| thetiscore |
0.2.1 |
Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects. |
2024-06-11 14:25:49 |
| error-parity |
0.3.11 |
Achieve error-rate parity between protected groups for any predictor |
2024-04-26 09:42:56 |
| fairness-datasets |
0.4.0 |
PyTorch dataset wrapper for the |
2024-04-18 21:31:52 |