Four features standing in for age explain why older cancelers went unflagged, and an MT362 Unit 7 bias review removes two, reruns the backtest and prices the fix. Searches like "mt 362 unit 7 assignment example", "mt362 unit 7 sample" and "mt362 unit 7 example" land here.
What a finished MT362 Unit 7 bias review looks like
Seven pages, two tables and one chart. Why the audit needs the attribute the model excludes is settled in the method section: birth years held for roughly 27,100 subscribers, a little over a third of them, are joined to backtest scores for audit use only. Table 1 reports flag rates by age band, overall and among subscribers who actually canceled within 90 days, since missing a real canceler is the harm that matters here. Table 2 lists candidate proxies with their correlation to age and their contribution to the score: tenure over ten years, email addresses at legacy internet-provider domains, paper catalog opt-in and orders placed by phone. The chart shows detection by age band before and after removing the catalog flag and the email-domain feature, lifting the ratio between oldest and youngest bands to 0.88 from 0.62.
How a MT362 Unit 7 example is structured
Harm definition comes first, because a bias review measures nothing until it says what unequal treatment would look like. Here the review rejects plain flag rates as the test, since older subscribers cancel less and a model should flag them less often, and chooses detection among actual cancelers instead: a loyal 70-year-old who leaves without a save offer is the case the chain never intended. Proxies are then traced one at a time, each judged on whether it predicts cancellation for reasons other than age and how closely it tracks age. Tenure survives; the catalog flag and email domain do not. Frameworks are named with dates, NIST Special Publication 1270 of March 2022 and the AI RMF 1.0 of January 2023, and the four-fifths ratio is borrowed as a screen, labeled as employment law's figure rather than a marketing rule.
Why the audit needs the excluded field
Removing birth year made age invisible to the model and to anyone checking it. The review joins the field back for audit only, under a written restriction, and argues that fairness cannot be tested on an attribute nobody can see.
Detection, not flag rates
Older subscribers cancel less, so fewer flags for them could be accurate. Among those who did cancel, though, the model caught 61 percent under 45 and 38 percent at 65 and over, and the missed customers left without any save offer.
Four stand-ins for age
Tenure over ten years, legacy provider email domains, paper catalog opt-in and phone orders all track age. Each is tested for whether it predicts cancellation on its own merits; tenure and phone orders do, while the catalog flag and email domain barely do.
Two features out, one point lost
Dropping the catalog flag and email domain lifts detection among older cancelers to 51 percent against 58 percent for the youngest band, a ratio of 0.88, while overall detection falls from 55 to 54 percent.
A test that repeats
The review asks for the same audit each quarter and whenever the vendor retrains the model, owned by the analytics lead, with a 0.8 ratio as the threshold that pauses offers pending review.
Where marks go in MT362 Unit 7
Reviews concluding the model is fair because it never sees the protected attribute are what this MT362 unit exists to catch, and graders mark that argument down wherever it appears. Stronger papers define the harm before measuring it and choose a test that fits the decision: for a save offer, missing real cancelers in one group matters more than raw flag counts. Proxy tracing earns credit when each feature is judged on whether it predicts the outcome for reasons of its own, not simply dropped for correlating with age. Borrowed thresholds need their origin stated, and a four-fifths figure presented as a marketing law misstates its source. A fix whose cost is measured rather than assumed also earns credit, as does a named owner who repeats the audit after every retraining.
Get a MT362 Unit 7 example written to your instructions
Share the Unit 7 model or decision, the attribute it leaves out and any figures broken down by group. Include the prompt and rubric too. A first bias review, free and back in 24-48h, defines the harm before measuring it and tests each proxy on whether it earns its place.
MT362 Unit 7 questions, answered
How can I test for bias if the case has no demographic data?
Say what data the audit would need and how the business could obtain it lawfully, such as a voluntary survey or an existing field held for another purpose. Some sections accept a designed audit rather than a performed one. Aggregate comparisons, such as offer rates by ZIP code against census income figures, can also serve as a first screen.
Which fairness measure should I use?
The one that matches the harm in your case. Where missing someone is the harm, compare detection among people who actually had the outcome; where exposure to an offer or a price is the issue, compare rates directly. State why you chose the measure, since graders tend to reward the reasoning over any particular formula.
Is using ZIP code in a model always biased?
Not always, but it deserves testing, because location often tracks income, race and age closely. Ask whether it predicts the outcome for a reason the business can defend, such as delivery cost, and compare results across areas with different demographics. Keeping it with a tested justification reads better than either dropping it reflexively or ignoring the question.