基于区块链的毕业设计An Open Standard for Ethical Enterprise-Grade AI – 道德企业级人工智能的开放标准

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An Open Standard for Ethical Enterprise-Grade AI

This is the result of our thoughts, at Zelros, to define an open standard for a transparent use of machine learning algorithms in enterprises.

You can find the story behind this standard in this blog post.

What is this standard about?

The standard we propose takes the form of a ‘check-list’, that every machine learning model embedded in a product or a service should document. We believe that filling this standard document for each AI model in production contributes to their traceability, compliance and transparency. The standard has seven sections, explained in the blog post.

Why publishing this standard on github?

We decided to publish this standard publicly on our github, because we know that is is only an imperfect preliminary version. It has to be reviewed by external contributors to be improved. By making our standard public, we hope it will gain visibility, and attract as much feedback as possible. We would love to learn:

  • how often can the standard be used?
  • in which cases is the standard not adapted?
  • what is missing in the standard?

What you’ll find here

In this repository, you can find:

  • the standard itself: standard.md
  • an example of populated standard document, for a fake machine learning project: project-example.md

How you can contribute

You can contribute by:

  • suggesting PRs (pull requests)
  • writing at this address

An Open Standard for Ethical Enterprise-Grade AI

This is the result of our thoughts, at Zelros, to define an open standard for a transparent use of machine learning algorithms in enterprises.

You can find the story behind this standard in this blog post.

What is this standard about?

The standard we propose takes the form of a ‘check-list’, that every machine learning model embedded in a product or a service should document. We believe that filling this standard document for each AI model in production contributes to their traceability, compliance and transparency. The standard has seven sections, explained in the blog post.

Why publishing this standard on github?

We decided to publish this standard publicly on our github, because we know that is is only an imperfect preliminary version. It has to be reviewed by external contributors to be improved. By making our standard public, we hope it will gain visibility, and attract as much feedback as possible. We would love to learn:

  • how often can the standard be used?
  • in which cases is the standard not adapted?
  • what is missing in the standard?

What you’ll find here

In this repository, you can find:

  • the standard itself: standard.md
  • an example of populated standard document, for a fake machine learning project: project-example.md

How you can contribute

You can contribute by:

  • suggesting PRs (pull requests)
  • writing at this address

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