Ethics & Transparency Management

From The Foundation for Best Practices in Machine Learning
Organisation Best Practices > Ethics & Transparency Management
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Ethics & Transparency Management[edit]

Objective
To ensure that Products are transparent and ethical.
Item nr. Item Name and Page Control Aim
12.1. Ethics Policy

An ethics policy, which promotes Ethical Practices in Machine Learning, ought to be derived by Data Science Managers and approved by the Management Committee and Ethics Committee. The Ethics Policy must be communicated to the Public.

To ensure that Machine Learning is designed, developed and implemented in accordance with the Ethical Practices.

12.2. Review of the Ethics Policy

The Ethics Policy should be reviewed periodically, or if significant changes occur, by Data Science Managers to ensure its continued effectiveness, suitability, and accuracy.

To ensure that the Ethics Policy is kept up-to-date.

12.3. Transparency Policy

A Public Interest and Transparency Policy, which promotes Public engagement, regulator engagement, and Transparency in Machine Learning, ought to be derived by Data Science Managers and approved by the Management Committee and Ethics Committee. The Transparency Policy must be communicated to the Public.

To ensure that Machine Learning is made transparent to the Public and is designed, developed and implemented in accordance with the Public Interest.

12.4. Review of the Transparency Policy

The Transparency Policy should be reviewed periodically, or if significant changes occur, by Data Science Managers to ensure its continued effectiveness, suitability, and accuracy.

To ensure that the Transparency Policy is kept up-to-date.

12.5. Speaking-Out Policy

A policy, which promotes Product Teams and/or Product Teams members to speak-out against unethical practices, ought to be derived by Data Science Managers and approved by the Management Committee and Ethics Committee.

To ensure that Product Teams and/or Product Teams members have a safe space to voice their concerns about Machine Learning and/or Products practices and/or decisions.

12.6. Review of the Speaking-Out Policy

The Speaking-Out Policy should be reviewed periodically, or if significant changes occur, by Data Science Managers to ensure its continued effectiveness, suitability, and accuracy.

To ensure that the Speaking-Out Policy is kept up-to-date.

12.7. Contact with Authorities

Appropriate contact with relevant sector authorities regarding Products, and their implementation, should be maintained. Products and Product Teams ought to work in close collaboration with relevant sector authorities in a collaborative and bona fide manner.

To ensure awareness and oversight of Products by authorities.