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STAV: System Trustworthiness and Accountability Vocabulary

Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. PyPI - Version PyPI - Python Version


This work is still under development.

stav:

  • Norwegian: to spell (words)
  • Swedish: letter (alphabet)
  • Slovak: state (condition)
  • Czech: stance

STAV Taxonomy

Available at https://w3id.org/stav

STAV Python module

STAV terms can also be accessible through a Python module called stav, available freely on the Python Package Index.

STAV class names are accessible through constants in the stav module. These class names are in capital letters with underscores separating words, LIKE_THIS, as stated in PEP 8. Values of these constants are simply a STAV class name, a string in CamelCase.

For example, stav.INSTRUCTIONS_FOR_USE is a string with value of "InstructionsForUse". (In the future, it should be able to configure the casing to "instructions_of_use", etc.)

With this, it will make the standardization of documentation within an organization, or across organizations, easier and can facilitate the use of the terms in MLOps settings, where data scientists and data engineers can use STAV terms as keys in their model logging and registration.

Watch the video

Installation

pip install stav

Use with MLflow

from mlflow import log_artifact, log_metric, log_param, set_tag
import stav

with mlflow.start_run():
    mlflow.set_tag(stav.INFO_TRAINING, "Basic LR model for iris data")
    mlflow.set_tag(stav.AI_PROVIDER, "Acme Corporation")
    mlflow.set_tag(stav.AI_DEPLOYER, "Sirius Cybernetics")
    mlflow.set_tag(stav.USE_SENSITIVE_PERSONAL_INFO, "No")

    mlflow.log_metric(stav.METRICS_ACCURACY, accuracy)

Sister projects

STAV (a Vocabulary) and STAP (an ODRL Profile) are sisters for system trustworthiness and accountability.

License

STAV: System Trustworthiness and Accountability Vocabulary by Arthit Suriyawongkul is licensed under CC BY 4.0

The stav Python module is distributed under the terms of the Apache License 2.0.

This work is conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224 and with the organizational support from members of these research groups:

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Members of RegTech group at ADAPT Centre contribute to AI and data ontology projects below, and they are may be of your interest: