Community meeting: TrustyAI introduction

[Rui Vieira]

On behalf of the TrustyAI team, I would like to thank you all for the opportunity to present the TrustyAI project and discuss the fit with Kubeflow at the community meeting.

Any feedback would be greatly appreciated.

TrustyAI summary

TrustyAI is an open-source community dedicated to providing a diverse toolkit for responsible AI development and deployment. TrustyAI was founded in 2019 as part of Kogito, an open-source business automation community, as a response to growing demand from users in highly regulated industries such as financial services and healthcare.

The TrustyAI community maintains a number of projects within the responsible AI field, mostly revolving around model explainability, model monitoring, and responsible model serving.

TrustyAI provides tools to apply explainability, inspect bias/fairness, monitor data drift and mitigate harmful content for a number of different user profiles. For Java developers, we provide the TrustyAI Java library containing TrustyAI’s core algorithms. For data scientists and developers that are using Python, we expose our Java library via the TrustyAI Python library, which fuses the advantages of Java’s speed to the familiarity of Python. Here, TrustyAI’s algorithms are integrated with common data science libraries like Numpy and Pandas. Future work is planned to add native Python algorithms to the library, such as to broaden TrustyAI’s compatibility by integrating with libraries like Pytorch and Tensorflow. One such nascent project is trustyai-detoxify, a module within the TrustyAI Python library that provides guardrails, toxic language detection, and rephrasing capabilities for use with LLMs. 

For enterprise and MLOps use-cases, TrustyAI provides the TrustyAI Kubernetes Service and Operator which serves TrustyAI bias, explainability, and drift algorithms within Kubernetes. Both the service and operator are integrated into Open Data Hub (ODH) to facilitate coordination between model servers and TrustyAI, bringing easy access to our responsible AI toolkit to users of both platforms. Currently, the TrustyAI Kubernetes service supports tabular models served in KServe or ModelMesh.

Potential integrations with Kubeflow

• Pre-built TrustyAI Kubeflow Notebooks image (already available in Open Data Hub)

• KServe integration that provides explanations for predictions made by AI/ML models using the built-in KServe explainer support for LIME and SHAP

• Integration with Model Registry to leverage model metadata, model lineage

• Feast

  • The SHAP algorithm requires a set of background (baseline) data to perform explanations. Feature stores are a natural fit for this purpose

  • Data source for establishing a baseline for data drift metrics

• Kubeflow Pipelines, as a pipeline step to perform

  • Real-time bias/fairness calculations

  • Data drift

  • Toxic content filtering/scoring/masking

  • Global explainability

Presentation

• TrustyAI introduction, presentation to the Kubeflow community, 9th July 2024

All the best,

TrustyAI team.