Data integrity means keeping information accurate, trustworthy, and intact as it is processed, stored, reviewed, and maintained. AI-generated changes and database operations can introduce subtle errors, so people must verify important results and preserve clear records. section 6.2 section 10.5
Protect integrity with practical controls:
- Record which AI model processed a document, when it ran, and who verified the output. section 6.2
- Version documents, label AI-processed sections, and store earlier versions securely. section 6.2
- Run backups, integrity checks, index maintenance, and statistics updates as appropriate for database maintenance. section 10.5
- Treat generated database queries as suggestions: supply the data dictionary and check table and column names before use. section 10.5
Table 6.2 Ethics in AI document handling
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Ethical concern
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Best practices
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|---|---|
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Data privacy
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Only use AI tools your company approves for sensitive files. Skip free public AI services for work tasks.
|
|
Transparency
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Tell your team when you use AI on shared documents. Be open about how AI helped.
|
|
Human oversight
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Check AI’s work yourself, especially for important documents. AI helps but doesn’t replace your judgment.
|
|
Bias awareness
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Watch for unfair or slanted results. AI can show bias or make mistakes, just like people do.
|
|
Intellectual property
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Be careful when creating content with AI. Make sure you’re not accidentally using someone else’s work.
|
|
Accountability
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Own your work, even when using AI. Don’t blame the AI if something goes wrong.
|
|
Continuous learning
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Keep up with AI best practices. The field changes fast, so stay current on using AI ethically.
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Use the ethics checklist to review privacy, transparency, human oversight, bias, accountability, and related safeguards around AI-processed documents. section 6.2
