Responsible AI

BUILD WITH AI.
TAKE RESPONSIBILITY FOR THE RESULT.

AI can help you create and automate work, but you remain responsible for the choices you make and the outputs you share. These principles guide responsible learning and project work with UpShift.

Principles

Eight principles for AI project work.

  1. 1. Use data with permission.

    Work with public, synthetic or authorised material and follow the restrictions that apply to it.

  2. 2. Protect personal and confidential information.

    Avoid entering sensitive data into an AI tool without the necessary permission and suitable controls.

  3. 3. Check important outputs.

    Review accuracy, sources and fitness for purpose before relying on or publishing AI-generated work.

  4. 4. Respect ownership and attribution.

    Follow applicable usage rights and acknowledge the sources and contributions that shaped your work.

  5. 5. Explain your use of AI.

    Make your own contribution and the role of AI clear in your portfolio and assessments.

  6. 6. Look for unfair or harmful results.

    Review outputs for bias, exclusion and harm, and record concerns rather than hiding them.

  7. 7. Keep people in control.

    Define approval points and permission limits before a workflow takes consequential actions.

  8. 8. Test within a clear scope.

    Check failure cases and document limitations before sharing or extending a project’s use.

Learner expectation: Use authorised materials, record your process, explain your contribution and submit work you have reviewed against the brief and these principles.

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