Qanary Explainability Demo

Explanations are of crucial importance in enhancing not only but primarily both, the trustworthiness of a wide range of systems and the traceability of responses. With the ever-increasing complexity of software as well as the recent and fast progress in artificial intelligence (AI), systems become more and more opaque to both, the user and the developer. For both parties, such black-box systems are problematic. Therefore, in particular, in the area of AI, explainability plays an increasingly important role.

In this demo, we present an approach for post-hoc explainability within component-based AI-enhanced software systems, i.e. the explainability of the behavior of components in general (not limited to AI models). To do this, we rely on the system’s data that was processed and/or is reflecting the intermediate processing steps. Accordingly, (post-hoc) explanations are created for each component, regardless of whether or not it is a black-box component.

The demo is based on any Qanary system and built with Python using the Streamlit library.

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Table of Contents

• Online Demo
• Building and running the application
  • Running locally with Python
    • Install dependencies
    • Run the Application
  • Running with Docker
    • Build Docker Image
    • Run Docker Image
• Cite
• Contribute
• Disclaimer

Online Demo

This demo is available at https://wse-research.org/qanary-explanations

Building and running the application

Running locally with Python

Install dependencies

pip install -r requirements.txt

Note: If you are using a virtual environment, make sure to activate it before running the command.

Run the Application

python -m streamlit run qanary-explainability-frontend.py --server.port=8501

After that, you can access the application at http://localhost:8501.

Running with Docker

The application is available at Dockerhub for free use in your environment.

Build Docker Image

docker build -t qanary-explainability-frontend:latest .

Run Docker Image

docker run --rm -p 8501:8501 --name qanary-explainability-frontend --env-file=service_config/files/env qanary-explainability-frontend:latest

Now, you can access the application at http://localhost:8501.

Cite

To be done

Contribute

We are happy to receive your contributions. Please create a pull request or an issue. As this tool is published under the MIT license, feel free to fork it and use it in your own projects.

Disclaimer

This tool is provided "as is" and without any warranty, express or implied.