https://github.com/trustyai-explainability/trustyai-explainability
TrustyAI Explainability Toolkit
https://github.com/trustyai-explainability/trustyai-explainability
explainability explainableai hacktoberfest interpretability java python xai xai-library
Last synced: 3 months ago
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TrustyAI Explainability Toolkit
- Host: GitHub
- URL: https://github.com/trustyai-explainability/trustyai-explainability
- Owner: trustyai-explainability
- License: apache-2.0
- Created: 2022-06-21T12:31:24.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-07-07T13:51:48.000Z (3 months ago)
- Last Synced: 2025-07-07T14:57:28.343Z (3 months ago)
- Topics: explainability, explainableai, hacktoberfest, interpretability, java, python, xai, xai-library
- Language: Java
- Homepage:
- Size: 19 MB
- Stars: 43
- Watchers: 6
- Forks: 43
- Open Issues: 76
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: CODEOWNERS
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README
# TrustyAI Explainability
This repo is the main hub for TrustyAI, containing the core Java library as well as various modules
to support the [TrustyAI Service](https://github.com/trustyai-explainability/trustyai-explainability/tree/main/explainability-service), [TrustyAI Operator](https://github.com/trustyai-explainability/trustyai-service-operator),
and [TrustyAI Python Library](https://github.com/trustyai-explainability/trustyai-explainability-python).
## Overview
TrustyAI is, at its core, a Java library and service for Explainable AI (XAI). TrustyAI offers fairness metrics, explainable AI algorithms,
and various other XAI tools at a library-level as well as a containerized service and Kubernetes deployment.
## Directory
- [explainability-core](https://github.com/trustyai-explainability/trustyai-explainability/tree/main/explainability-core), the core TrustyAI Java module, containing fairness metrics, AI explainers, and other XAI utilities.
- [explainability-service](https://github.com/trustyai-explainability/trustyai-explainability/tree/main/explainability-service), TrustyAI-as-a-service, a REST service for fairness metrics and explainability algorithms including [ModelMesh](https://github.com/kserve/modelmesh) integration.
- [explainability-arrow](https://github.com/trustyai-explainability/trustyai-explainability/tree/main/explainability-arrow), a Java module to facilitate the communication between TrustyAI-Java and TrustyAI-Python using Arrow.
- [explainability-connectors](https://github.com/trustyai-explainability/trustyai-explainability/tree/main/explainability-connectors), A Java module to interface with different black-box predictive model deployments or inference services. Includes support for [KServe](https://github.com/kserve) / [ModelMesh](https://github.com/kserve/modelmesh) via gRPC.
- [explainability-integrationtests](https://github.com/trustyai-explainability/trustyai-explainability/tree/main/explainability-integrationtests)
A set of integration tests for integrations within [Kogito](https://kogito.kie.org/), namely DMN, PMML, and OpenNLP models.
## Roadmap
[GitHub project page](https://github.com/orgs/trustyai-explainability/projects/10)
## For More Information
Our preprint [TrustyAI Explainability Toolkit](https://arxiv.org/abs/2104.12717)
is a great source of knowledge of what the core library can offer.
Furthermore, you can reach the dev team on:
* [ODH Community Slack](https://odh-io.slack.com/archives/C03UFCVFFEY)
* [Zulip](https://kie.zulipchat.com/#narrow/stream/232681-trusty-ai)
* or by coming to one of our [community meetings](https://github.com/trustyai-explainability/community#meetings)
## Building and Contributing
All contributions are welcome! Before you start please read the [contribution guide](CONTRIBUTING.md).