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AI use policy

As explained in Starting Data Analytics with Generative AI and Python

An AI use policy is a documented set of organizational rules and principles governing the ethical, responsible, and compliant use of artificial intelligence.

An AI use policy turns a vague warning that AI can make mistakes into practical rules for responsible use. It identifies which documents AI may access, who may use it, and which regulatory constraints apply. section 8.2.1

Keep the policy short, clear, and usable. Cover:

  • Data privacy and handling, security, transparency, accountability, and fairness. section 8.2.1 section 8.2.3
  • The policy’s scope, including employees, contractors, and partners involved in designing, deploying, or making decisions with AI. section 8.2.1
  • Accessible wording and maintenance practices so stakeholders can understand, contribute to, and update the policy. section 8.2.3

A practical starting point

A small enterprise planning an AI pilot might begin with a single-page policy stating its purpose, scope, principles, data boundaries, access expectations, and applicable legal constraints. Stakeholders can improve it over time using an accessible internal wiki containing anonymized examples of successful and failed prompts and responses. section 8.2.1 section 8.2.3

Treat the policy as a living document rather than a lengthy file that nobody reads. Healthcare, finance, and public-service applications may require stricter controls and additional ethical considerations. section 8.2.1 section 8.2.3

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Defines policy contents and scope, then stops short of prescribing one universal template for every organization or sector.
Examples
Uses a one-page pilot policy, an internal prompt-and-response wiki, and memorable privacy and security guidance.
What you can do after
Build it from scratch as a concise, accessible policy and maintain it with stakeholder contributions and practical examples.
How it connects
Connects AI use with data privacy, security, transparency, accountability, fairness, compliance, and stakeholder education.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.