Interpretability tools and their limits

Unit ID: ML-M09-U04 Estimated active time: 20-35 minutes

Why this matters

Responsible ML and Behaviour in Use helps learners avoid a common mistake: using a modelling technique without knowing what the result is allowed to mean.

In this unit, we focus on interpretability tools and their limits because it is one piece of the larger workflow.

The idea

Interpretability tools help explain behaviour, but they are not proof of cause.

The classroom rule is simple:

Do the smallest honest experiment, then explain the limit.

For this topic, that means connecting the code or worksheet output to the decision context. A number, cluster, threshold, or model comparison is not useful until the learner can say what it means and what it does not mean.

Predict

Before running code, answer:

• What output do you expect?
• What could make that output misleading?
• What would you check before trusting it?

Run or inspect

Use the supplied synthetic dataset for Module 09. Read the column names first. Identify the target if the module has one. If there is no target, say clearly that the exercise is exploratory.

Keep the result small enough to inspect: a compact table, one metric summary, one cluster summary, or one threshold comparison.

Change one thing

Change one modelling choice or interpretation choice. For example, change a threshold, remove a risky feature, compare a baseline, scale the data, or inspect one subgroup.

Then ask:

Did the conclusion change, or only the number?

Practice

Compare safe operational slices and write a monitoring and stop-condition memo.

Write a short note that names at least one limitation.

Check and explain

Complete this sentence:

This result suggests ______, but it does not prove ______.

Takeaway

Connect model performance to real-world use, oversight, monitoring, and limits. The model output is evidence to inspect, not a claim to overstate.