WRIT6000 Chap.4 Ethics, Peer Review and Responsible AI
Ethics, Peer Review and Responsible AI
Ethical writing shapes the whole communication process: audience selection, evidence, description of people, acknowledgement of uncertainty and responsibility for consequences. Accuracy is necessary but not sufficient. A factually correct document can still exclude readers, conceal a material limit, stereotype a group or direct action toward someone without authority.
This chapter turns broad values into concrete checks by asking who benefits, who carries risk, what the evidence can establish, whose perspective is absent and how a reader can seek correction. Peer review is part of that ethical system. Useful feedback identifies a passage, explains its consequence and proposes a feasible revision while leaving substantive ownership with the writer.
It distinguishes requirements from stylistic preferences and addresses the document rather than the writer's character. Generative AI adds another layer of responsibility because fluent output can contain invented facts, weak causal links, generic assumptions or unsuitable local detail.
Responsible use therefore requires checking claims against sources, comparing output with the actual rhetorical situation, documenting consequential human changes and acknowledging use as the task requires. A process record should show what the tool did, what was retained, what was rejected and who verified the result. Ethics becomes observable through these decisions.
It is not a declaration of good intention but a practice of evidence, consequence, revision, disclosure and accountable authorship.
What this chapter covers
- 01
Audience responsibility and possible harm
- 02
Evidence checking and visible limitations
- 03
Describe, evaluate and suggest feedback
- 04
Writer ownership in peer review
- 05
Permitted and prohibited AI roles
- 06
Verification, process logs and acknowledgement
Worked example · free
Correct an unsupported audience stereotype
- 1Describe the unsupported causal wording.
- 1Identify the evidence actually available.
- 1Explain the ethical and rhetorical consequence.
- 1Suggest a problem-focused revision.
- 1Leave the final drafting decision with the writer.
Key terms
- Professional ethics
- Principles guiding responsible evidence, representation, consequence and accountability in workplace communication.
- Peer review
- A structured process for describing, evaluating and suggesting changes to another writer's work.
- AI acknowledgement
- A specific account of the tool, purpose, output and human checking involved in a submission.
- Verification
- The direct checking of a claim, quotation or reference against a reliable original source.
- Process log
- A dated record of prompts, outputs, checks, revisions and final use.
Ethics, Peer Review and Responsible AI FAQ
What makes professional writing ethical?
Ethical writing uses accurate evidence, represents affected people responsibly, makes important limits visible and considers consequences beyond the intended reader. It also creates a route for clarification or correction. Good intention is not enough when the document's format, assumptions or claims still impose avoidable harm.
How can peer review be critical without being hostile?
Point to the passage, name the reader consequence and distinguish a required correction from a preference. Suggest a practical next step and explain the principle behind it. Address the document rather than the writer's character, while remaining direct about unsupported evidence, inaccessible design or misleading framing.
What should an AI acknowledgement include?
State the tool, the task it performed, the output retained and the human work used to verify, reject, revise or integrate that output. The statement must match the task's permission and your process record. A generic claim that AI helped does not explain authorship or responsibility.
Assessment move
Create an ethics checklist with six headings: audience, evidence, representation, possible harm, accessibility and accountability. Apply it to a short fictional workplace message. Under each heading, record one feature that works, one risk and one revision you could justify. For representation, separate what the evidence shows from assumptions about age, culture, disability, expertise or motivation.
For accountability, identify who can correct an error and how a reader reaches that person. Practise peer review with a three-part comment: describe the passage without judgment, evaluate its reader consequence using a named principle, and suggest a feasible next action. Label whether the issue is a requirement, an evidence problem or a preference.
Exchange comments and ask whether the reason is clear enough for the writer to accept, adapt or decline the advice responsibly. When a task permits generative AI, maintain a process log from the first prompt. Record the purpose of each use, the output considered, the verification source, the change made and the reason.
Test every generated citation, statistic, organisational fact and claim about a group directly against a source you have read. Then conduct a contextual review that fact checking alone cannot provide: does the draft address the right authority, fit the genre, respect the people described and make limits visible? Write an acknowledgement that accurately describes the tool's role and your human work.
Compare it with the log and repair any mismatch. Finish by selecting one accepted AI suggestion and one rejected suggestion, then explain the evidence and ethical principle behind each decision. That contrast demonstrates judgment more clearly than a generic statement that the output was checked.
