CaseAdvancedResponsible AI & Advanced Practice / AI product case study teardowns / #15
Compare how two products communicate AI uncertainty to users.
SPARK comparing Emberlyst Claims AI and Rooktrace Claims AI, two fraud-flagging tools piloted side by side
Bramwell Achike processes home-insurance claims, and his team ran a 90-day side-by-side pilot of two AI fraud-flagging tools before picking one: Emberlyst Claims AI, and Rooktrace Claims AI.
The direct answer
Rooktrace communicates uncertainty better, because it shows the specific reasons behind a flag instead of a bare number. Emberlyst's 94% confidence score gives an adjuster nothing to check against, so the first time it's wrong, trust collapses all at once instead of adjusting.
Do this, in order
Show named reasons behind a flag, not a bare confidence percentage.Why: a reason gives the adjuster something to check. A percentage only gives them something to obey or ignore.
Let the adjuster dismiss one reason without dismissing the whole flag.Why: a claim can be flagged for one weak reason and two strong ones, and a single override option throws out the strong ones too.
Track override rate by reason type, not just overall accuracy.Why: a 90% accurate tool can still be useless if adjusters override its one weakest reason every single time.
Never let the score-only version ship without at least one supporting fact attached.Why: a number with nothing behind it teaches an adjuster to either trust it blindly or ignore it entirely, no middle ground.
Keep a simple visual severity cue (a color, a weight) alongside the reasons.Why: reasons take a few seconds to read, and a fast glance still matters when the queue is fifty claims deep.
Don't build auto-approval on day one, no matter how good the reasons look.Why: showing good reasons is not the same as being right enough to skip a human, and those are two different bars to clear.
How to answer this, stage by stage
Six moves. This is a narrower comparison question, so the walkthrough stays short.
Stage 1
Scope it to two real products
Say it like this
"I'll compare two specific fraud-flagging tools an insurance team actually piloted side by side, Emberlyst and Rooktrace, not uncertainty UI in the abstract."
Why this works
A real side-by-side pilot gives you something concrete to judge instead of two hypotheticals.
Stage 2
Say your structure out loud
Say it like this
"I'll use SPARK: situation, the adjuster's day today. Payoff, the habit I want to build. Anchor, the one design decision that matters. Risk, what breaks when it's wrong. Keep out, what I wouldn't build yet."
Why this works
Shows the interviewer you're judging a design decision, not just reacting to two logos.
Stage 3
Name the anchor each product actually chose
Say it like this
"Emberlyst's anchor is a single number: 94% confidence. Rooktrace's anchor is a short list: three named reasons the claim got flagged. That one design choice is the whole comparison."
Why this works
Reduces two whole products down to the one decision the rest of the answer hangs on.
Stage 4
Say what breaks when each one is wrong
Say it like this
"When Emberlyst's score is wrong, the adjuster has nothing to check it against, so trust either snaps to zero or never adjusts at all. When Rooktrace's reasons are wrong, the adjuster can see which specific reason failed and just dismiss that one."
Why this works
This is the risk step, and it's what actually decides the comparison.
Stage 5
Give the direct answer
Say it like this
"Rooktrace communicates uncertainty better. Reasons survive a wrong flag. A bare score doesn't, it just teaches the adjuster to stop reading it at all."
Why this works
Matches deliverable 0, no hedging, no "it depends."
Stage 6
Name what you'd still keep out, and close
Say it like this
"Even Rooktrace's better design isn't ready for auto-approval on day one. Good reasons make a flag easier to check, they don't make the model right enough to skip a person."
Why this works
Shows judgment past the immediate comparison, toward what shouldn't ship yet either.
Let's learn
Both tools do the same job: scan a filed home-insurance claim, along with its photos and repair estimate, and flag the ones worth a closer look for possible fraud.
Before either tool, Bramwell's team reviewed every claim by hand, a flat process, no ranking, roughly 40 claims a day per adjuster, all treated the same.
With either AI tool running, adjusters get a shortlist instead: about 6 flagged claims a day, out of the 40, worth a closer look first.
Knowledge spark: what's a confidence score?
A number the model reports as its own guess at how sure it is. High usually means it thinks it's right. On its own, a confidence score says nothing about what specifically made the model suspicious.
The turn: the shortlist itself isn't what separated the two tools. What separated them was what happened the first time the shortlist was wrong.
Adjuster override rate, by tool, across the 90-day pilot
A 61% override rate means adjusters ignored Emberlyst's flag on nearly two out of three claims, score or no score.
At its worst: an adjuster stops reading Emberlyst's flags altogether, the queue fills with score-only alerts nobody trusts, and a genuinely fraudulent claim slides through in the exact week everyone's stopped checking the number at all.
The design decision that mattered most
Rooktrace's anchor is showing named reasons instead of a lone score. A reason gives the adjuster something concrete to verify against the actual claim file. A score gives them a single number to either obey completely or dismiss completely, with nothing in between.
What I would leave alone: the underlying fraud-detection model behind both tools is roughly comparable in raw accuracy. The comparison here isn't about which model is smarter, it's entirely about how each one talks to the person reading its output.
A wrong number teaches an adjuster to stop reading the number. A wrong reason teaches them which one reason to ignore next time.
The lesson: uncertainty communication isn't a UI detail bolted onto a model. It's the whole difference between a tool a person can keep calibrating and one they either worship or abandon.
Now here is the same thing as a story
Use the short version above when timed. Read this one for how the pilot actually played out, week by week.
Bramwell has adjusted home-insurance claims for eight years and can usually smell an inflated repair estimate before he's finished the first page.
Same underlying doubt, two very different things handed to the person who has to act on it.
Emberlyst ran first, days one through thirty. Its flags were fast and confident-looking: a claim, a photo, and a bold "94% confidence, likely fraud" stamped across the top.
The flagging step, third in line, is where the two tools' whole design difference actually lives.
The early weeks looked fine. Adjusters glanced at the score, mostly trusted it, moved fast through the shortlist. Then a genuinely ordinary claim, a kitchen fire with an unusually detailed but entirely legitimate repair estimate, got a 91% fraud score with no explanation attached.
By day sixty, the two tools' override rates had already split wide apart, weeks before the final decision.
Bramwell spent forty minutes manually reconstructing why the model might have flagged it, since the tool gave him nothing to go on directly. He found nothing suspicious. From that day on, he started skimming past Emberlyst's scores instead of reading them closely, on every claim, not just similar ones.
A bare score sits almost as low as no signal at all. Reasons are the only approach that lands in the calibrated, actionable corner.
Rooktrace started day thirty-one, running in parallel. Its flag on a similarly ambiguous claim read: "Flagged because: repair estimate is 3 times the regional average for this damage type; contractor listed has no prior claims history." Bramwell checked the contractor himself in two minutes and found a brand-new, unlicensed one. The flag held.
Four things Rooktrace's flag message includes that Emberlyst's single number never does.
The old flag asked Bramwell to trust an entire number or none of it. The new one asked him to check one specific claim against one specific fact, and let him agree with two reasons while dismissing a third.
The team's first instinct was to judge both tools purely on raw fraud-catch accuracy, since that's the number vendors led with in every sales call. It took watching Bramwell quietly stop reading Emberlyst's scores, not complain, just stop, to see that accuracy alone said nothing about whether a person would still be listening by day sixty.
SPARK, in one screenNot a features checklist. SPARK is what forces you to name the one decision the comparison actually turns on.
Five letters, and the anchor step is where the entire comparison between the two tools actually lives.
S
Situation.
Bramwell reviews about 40 claims a day, and either tool trims that to a 6-claim shortlist worth closer attention.
Grounds the comparison in one real adjuster's actual workload.
P
Payoff.
The habit worth building is calibrated trust, checking a flag when it earns a check, not blind faith in a number or blanket suspicion of every flag.
Names the actual habit a good uncertainty design should build.
A
Anchor.
Rooktrace shows named reasons behind a flag. Emberlyst shows one bare confidence percentage. This is the single decision the whole comparison hangs on.
The hardest, most load-bearing step, and the one an easy "just show a number" answer skips.
R
Risk.
When Emberlyst's score is wrong, there's nothing to check it against, so trust either snaps to zero or never adjusts. When Rooktrace's reasons are wrong, the adjuster dismisses just that one reason.
Shows the anchor surviving contact with an actual bad case, not just looking good in a demo.
K
Keep out.
No auto-approval or auto-denial on day one, even for Rooktrace. Better reasons make a flag easier to check, not automatically safe to skip checking.
Shows judgment about what not to build yet, not just what to build.
Claims caught vs. claims genuinely worth a second look, by week
Both tools flagged similar volumes. What actually diverged was whether adjusters kept agreeing the flags were worth their time.
The recap, one line per letter: situation is Bramwell's 40-claim daily queue, payoff is calibrated trust as the goal, anchor is reasons versus a bare score, risk is what happens the first time each one is wrong, and keep out is no auto-approval regardless of which design wins.
And if you want to be sure it really works, try it somewhere elseSame five letters, an event-ticketing platform instead of an insurer.
Sumter Vale runs fraud review for a mid-size ticketing platform. Quenby Adeoye leads the box-office fraud-review team, checking flagged high-value ticket purchases before they're fulfilled.
Mapped onto SPARK: situation is Quenby's team reviewing about 200 flagged purchases a day out of tens of thousands sold. Payoff is a habit of checking the specific signal, a mismatched billing region, a brand-new account, a resold-ticket pattern, rather than either trusting every flag or ignoring the queue. Anchor: the platform's AI shows a short tag like "new account, high-value, no history" instead of a lone risk score. Risk: when a legitimate fan buying their first-ever ticket gets flagged, the named reason lets Quenby's team see immediately it's just a new account, not a real red flag, and clear it in seconds. Keep out: no automatic purchase cancellation, even for a high-confidence flag, since a wrongly cancelled ticket during an on-sale rush becomes a public complaint fast.
The same four requirements hold for a ticketing fraud flag as for an insurance claim flag.
Swap the trigger and it still runs.
Speed: an interviewer caps you at sixty seconds. Say "Rooktrace wins, reasons survive a wrong flag, a bare score doesn't," and stop.
Cost: there's no engineering time this quarter to build a full reasons interface. Say so honestly, and start with just the single strongest reason shown as one short line, even without the full breakdown.
The model gets better, for real: if Emberlyst's underlying accuracy genuinely improves, that's still not a reason to skip reasons entirely, a more accurate score with no explanation still teaches adjusters to stop reading it the first time it's wrong.
Where people run it wrong.
They compare two AI products purely on raw model accuracy, ignoring how each one actually talks to the person using it.
They treat a confidence score as inherently more "scientific" than named reasons, when a score with nothing behind it is often less useful.
They assume better uncertainty communication means overwhelming detail, when three short, specific reasons beat a wall of ten vague ones.
How to use it live. When someone asks you to compare two AI products' uncertainty design, ask yourself: what does this person do the first time each one is wrong. Whichever design still gives them something to do next, not just something to feel, wins.
Flashcards (tap any card to flip it)
1 · THE FRAMEWORK
What framework fits "compare how two products communicate AI uncertainty to users"?
Tap to flip
ANSWER
SPARK: situation, payoff, anchor, risk, keep out. It's a design comparison, judged by which design decision survives being wrong.
2 · THE PERSON
Who is this answer about?
Tap to flip
ANSWER
Bramwell Achike, an insurance claims adjuster of eight years who ran a 90-day pilot comparing two fraud-flagging tools.
3 · THE ANCHOR
What's the one design decision the whole comparison turns on?
Tap to flip
ANSWER
Showing named reasons behind a flag (Rooktrace) versus showing a bare confidence percentage with nothing behind it (Emberlyst).
4 · THE RISK
What happens the first time each tool's flag is wrong?
Tap to flip
ANSWER
Emberlyst's wrong score gives the adjuster nothing to check, so trust snaps or never adjusts. Rooktrace's wrong reason lets the adjuster dismiss just that one reason.
5 · THE DIRECT ANSWER
Which tool wins, and why, in one sentence?
Tap to flip
ANSWER
Rooktrace, because reasons survive a wrong flag while a bare score just teaches the adjuster to stop reading it.
6 · THE NUMBER
Fill in the blank: Emberlyst's override rate was ___%, versus Rooktrace's 19%.
Tap to flip
ANSWER
61%. Adjusters ignored Emberlyst's flag on nearly two out of every three claims.
7 · THE KEEP OUT
What does this answer say not to build yet, even for the winning design?
Tap to flip
ANSWER
Auto-approval or auto-denial on day one. Good reasons make a flag easier to check, not safe to skip checking.
8 · CROSS PRODUCT TRANSFER
Section 4 answers this again for a different product. Which product, and what's its equivalent of "named reasons"?
Tap to flip
ANSWER
Sumter Vale's ticketing fraud review. Its reason tag reads something like "new account, high-value, no history" instead of a lone risk score.
Check yourself Score: 0 / 0
Short answer, name the anchor
1. What single design decision separates Emberlyst from Rooktrace, and why does it matter more than raw model accuracy?
Show hint
Look at the Anchor step and the comparison diagram.
Show answer
Model answer: Rooktrace shows named reasons, Emberlyst shows a bare score. It matters more than accuracy because it decides whether an adjuster can keep calibrating trust after a wrong flag, or just stops reading it.
Multiple choice
2. Why did Bramwell's team start skimming past Emberlyst's scores after the kitchen-fire claim?
A. Emberlyst's model accuracy had gotten measurably worse.
B. The 94% score gave no reason to check against, so a wrong one taught them to stop trusting the number.
C. Rooktrace's pricing was cheaper, so the team switched immediately.
D. Emberlyst's flags were too slow to load.
Show hint
Look at the story's trigger moment.
Show answer
B. A wrong number with nothing behind it teaches the reader to stop reading the number at all.
True or false
3. True or false: this answer recommends auto-approving claims that Rooktrace's reasons clear as low risk.
True
False
Show hint
Look at the Keep out step.
Show answer
False. No auto-approval on day one, even for the better-designed tool. A person still reviews every flag.
Fill in the blank
4. Fill in the blank: Rooktrace's override rate held at 19%, while Emberlyst's climbed to ___%.
Show hint
Look at the grouped-bar chart of override rates.
Show answer
61%. A gap that opened by around day sixty of the ninety-day pilot.
Short answer, apply it yourself
5. Pick an AI product you use yourself. Does it show you a bare confidence signal, or does it show you a reason? What would change if it swapped to the other one?
Show hint
Think of a spam filter, a spell checker, or a recommendation you've seen with "you might like this."
Show answer
Model answer: Many people point to spam filters that just move mail without explanation. A named reason, like "sender domain flagged," would make a wrongly filtered email far easier to trust or dispute.
Short answer, where it wouldn't matter
6. Name a situation where showing a bare confidence score instead of reasons genuinely wouldn't cause a problem.
Show hint
Think about low-stakes, easily reversible decisions.
Show answer
Model answer: A low-stakes recommendation, like a music suggestion, where being wrong costs the user nothing more than skipping a song.
Before you close the answer
Why this works
Tests whether you can judge two real products by the single design decision that actually decides trust over time, instead of comparing them on marketing accuracy claims.
Follow-up traps
"Isn't showing three reasons just slower for adjusters to read than one number?" Response: a few extra seconds per flag is cheap compared to the cost of an adjuster who's stopped reading flags at all after week eight.
"What if Rooktrace's reasons themselves turn out to be wrong sometimes?" Response: they will be sometimes, and that's fine, because a wrong reason is checkable and dismissible one at a time, while a wrong bare score just discredits the whole flag.
If pressed
Rooktrace's real reason-ranking model actually runs a lighter-weight check than Emberlyst's single score, since generating three specific, verifiable reasons costs less computation than the deeper ensemble Emberlyst uses to produce one polished-looking percentage.
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