Lesson 2 of 12

Structured learning draft

Harms with Bias

In AI Ethics: Principles & Practice, the way a learner handles harms shapes how Bias is used and evaluated. Harm analysis considers severity, scale, duration and distribution. This beginner lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain harms in the context of AI Ethics: Principles & Practice.
Apply Bias to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureDecision matrix

Harms: from context to evidence

Harms connects affected people and context to a documented mitigation in AI Ethics: Principles & Practice.

Affected people and context

Define the purpose, intended user and Bias constraints.

frames

Harms

Write concrete scenarios naming affected people.

produces evidence for

Documented mitigation

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Harms is credible only when the result can be traced back to its purpose, inputs and constraints. Harms is the decision layer between the starting context and evidence that the result is fit for purpose.

Harm analysis considers severity, scale, duration and distribution. For Bias, distinguish performing an operation from demonstrating that it suits the stated purpose. Write concrete scenarios naming affected people. Record assumptions that could change the conclusion.

Apply harms deliberately

State the AI Ethics: Principles & Practice task and the decision it supports.
Prepare a small Bias case with a known input and difficult boundary.
Write concrete scenarios naming affected people.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

harms evidence record
Review point	Evidence
Purpose	The specific Bias outcome and intended user
Method	The harms decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using Bias before defining what harms must achieve.
Checking only the easiest AI Ethics: Principles & Practice example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For AI Ethics: Principles & Practice, complete a bounded Bias task demonstrating harms. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In AI Ethics: Principles & Practice, which evidence best supports a harms result produced with Bias?

A polished Bias output without its input The input, expected behaviour, normal and boundary results, and a limitation A screenshot showing the tool opened A claim that the method always works

Lesson summary

For AI Ethics: Principles & Practice, harms means: Harm analysis considers severity, scale, duration and distribution.
A credible Bias result includes a checked boundary, not only a successful example.
The next lesson builds on this harms evidence record.

Sources and further reading

Recommendation on the Ethics of AIUNESCO - accessed 2026-08-21
AI PrinciplesOECD - accessed 2026-08-21

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