Responsible Use of Digital Intelligence
Keep a human responsible for what AI does.
Every AI workflow needs a person who can define its boundaries, check its work, and respond when something goes wrong. That responsibility remains when a task runs automatically.
The RESPECT framework
Make responsible AI part of the work.
RUDI uses RESPECT to connect human-centered AI principles to decisions about training, tools, data, and digital workers.
Read the framework and workplace examples ↗- R · Responsible
- A named person manages the work and remains accountable for its quality and consequences.
- E · Equitable access
- People receive tools, accessible training, and support that reflect their different starting points.
- S · Safety
- Teams test foreseeable failures, set limits, and define when a person must intervene.
- P · Privacy
- AI uses approved data with appropriate permissions, retention, and sharing rules.
- E · Efficient and effective
- The result improves the task after preparation, review, and rework are counted.
- C · Control
- People can question, correct, override, and stop AI-assisted work.
- T · Transparency
- People know when AI is involved, what it does, and how to reach a human.
Human management
Define what the person in charge can do.
Name the owner and the job.
Agree on the purpose, permitted actions, quality expectations, and person responsible. Make clear who can approve changes or additional access.
Make review and intervention practical.
Give the owner enough information, time, and authority to check results, investigate errors, and pause the workflow. Match review requirements to the consequences of a mistake.
Provide a route for correction.
People affected by an AI output need to know how to report a problem, challenge the result, and get a response from someone responsible for resolving it.
How RUDI helps
Put these decisions into training and everyday practice.
Policies people can use.
Define approved tools and information, review requirements, disclosure, and escalation. Use examples from the team’s work to make the expectations understandable.
Explore workspace setup and policy supportTraining with support for different starting points.
Teach people how to check outputs, protect data, recognize limitations, and get help. Adapt practice and support to roles, experience, and accessibility needs.
Explore team trainingWorkflows with clear responsibility.
Map the task with the people who do it, check the proposed design using RESPECT, and test quality and total effort before expanding use.
Explore department workflowsStart with your team
Define how your organization will use AI responsibly.
Bring your current tools, a proposed workflow, or the policy questions your people are facing.
Discuss responsible AI