Good uses of AI

AI doing small jobs well.

I collect real examples of AI doing a narrow job that makes life better. For each one I check three things: does it work, who pays, and what can go wrong. Some well-known examples turn out to be unproven, and I say so.

Why I keep this list

I choose to be an optimist about AI. I believe it can raise the quality of what we make and give more people work that is creative, fulfilling and useful. The other path is the dystopian one: cheaper and cheaper goods, more layoffs, and a shrinking workforce.

Optimism is a direction, not a forecast. This list keeps mine honest. Each entry shows what the AI actually does, where a person makes the call, and what the best evidence says, including when the evidence says "not yet."

The good-use test

Seven questions I ask of any AI use.

  1. Is the harm real and measured? A number for how often it happens and who it hurts.
  2. Is the AI's job narrow and checkable? It spots, reads, flags or sorts, and a person can check every output.
  3. Does a person make the decision that affects someone? Before any fine, denial or treatment.
  4. Does it change outcomes, by evidence from someone without a stake? Not only the seller's or the buyer's report.
  5. Is it fair to the people it touches? Known error rates, a way to appeal, limits on data kept.
  6. Is it built to succeed, even when success shrinks the revenue? If 90% of drivers stop for a school bus today and 99% stop later, ticket revenue falls. That's the program working. Good designs make sure nobody needs violations to stay high.
  7. Is it open about how it works? Rules, accuracy and results the public can read.

How I grade sources: A is official statistics or peer-reviewed research with published methods. B is a large study with published methods, including trials paid for by the people who make the tool. This page uses only A and B sources.

Case study

Cameras on school buses

Verdict: promising, not proven

When a school bus stops and its stop arm swings out, a camera on the side starts recording. Software flags any car that drives past and reads its plate. A vendor employee screens the flag, a police officer approves or rejects it, and a civil ticket goes to the car's owner. The AI does the first sort. People decide who gets a ticket. (NHTSA, 2024, B)

The problem

In one day in spring 2026, about 101,000 bus drivers in 39 states and DC counted 56,024 cars illegally passing their stopped buses. (NASDPTS, 2026, B)

Deaths are rarer than that number suggests, and most aren't caused by passing cars. From 2015 to 2024, 77 school-age pedestrians died in school-transportation crashes. A school bus hit 44 of them, and other vehicles hit 33. (NHTSA, 2026, A) In the 2024-25 school year, all six deaths reported in school bus loading zones were caused by the bus itself. (Kansas State Department of Education national survey, 2025, B)

Does it work?

Cameras catch far more drivers than police or bus drivers can. Whether they change behavior is still open. Two federally funded studies, in five districts across four states, could not show a clear drop in passing. (NHTSA, 2021, B; NHTSA, 2024, B) In the 2024 study, the camera operator shared only combined totals, so the researchers couldn't check its numbers. No company publishes how often its system flags the wrong car.

What went wrong in Dallas

A camera company's CEO admitted paying local officials more than $3.5 million to win Dallas County Schools' stop-arm camera contracts. Dallas County Schools was shut down in 2017 with about $103 million in debt. (US Attorney, Northern District of Texas, 2019, A) The lesson isn't about cameras. It's about money: when someone's income depends on more tickets and more contracts, watch the incentives.

Here in Colorado

Colorado allowed these cameras in May 2025. The fine is capped at $300, the school board has to approve, and the vendor's pay can't be based on fines or on ticket quotas. (Colorado HB25-1230, A) As of November 2026 I found no Colorado program issuing tickets yet.

What would make it a clear good use

  • Share raw data with independent evaluators, so someone without a stake can measure the effect.
  • Publish how often the system flags the wrong car, and how often police reject a flag.
  • Keep footage only when there's a violation, and only for enforcement.
  • Look at the bus too. Most children killed at bus stops are hit by the bus itself, so a system that sees children in a bus's blind spots may save more lives than any ticket.

Going deeper: paying for the good

If the cameras work, fewer drivers pass, fines fall, and the camera company earns less. I think that's what success looks like, as long as nobody depends on the fines. I wrote a short essay on why, with an interactive model you can adjust and a list of who to read.

Read the essay and try the model

More good uses

Eye screening for diabetes

The AI's job: reads photos of the back of the eye at a regular doctor's visit and says "see an eye doctor" or "retest in a year."

Evidence: in a randomized trial, all of the young people offered the AI screen completed the eye exam, against 22% with a normal referral. (Nature Communications, 2024, A)

Watch for: the accuracy trials in adults were paid for by the makers.

School-zone speed cameras

The AI's job: small. The camera measures speed and software reads the plate. A person reviews every ticket.

Evidence: across 1,821 New York City cameras, speeding tickets fell 75% and crashes fell 14%. (Transportation Research Interdisciplinary Perspectives, 2025, A)

Watch for: who pays the fines. The vendor gets no share in New York. That's a good design.

Finding methane leaks

The AI's job: finds methane plumes in satellite and aircraft images and ties each to a facility. People confirm and fix the leak.

Evidence: satellites found very large leaks making up about 8% to 12% of oil and gas methane emissions. (Science, 2022, A)

Watch for: spotting a leak doesn't fix it. Repairs follow when someone requires them.

Sepsis warnings in hospitals

The AI's job: reads the health record every few minutes and alerts a clinician when sepsis looks likely. The clinician decides.

Evidence: at five hospitals, patients whose alert was confirmed quickly had 3.3 points lower death rates. (Nature Medicine, 2022, B)

Watch for: not randomized, and the authors have a stake. See Epic's model below.

Flood forecasts for rivers with no gauges

The AI's job: predicts river floods up to five days ahead where no gauge exists. Agencies and residents decide what to do.

Evidence: five-day forecasts were as reliable as the main public benchmark's same-day forecasts. (Nature, 2024, B)

Watch for: forecasts are measured; lives saved aren't yet.

Near-miss video for safer streets

The AI's job: tracks cars, bikes and people in video and measures how close they came to colliding. Engineers choose the fix. No one is fined.

Evidence: early. A state DOT test found one tool only moderately accurate. (Kansas DOT, 2023, B)

Watch for: nobody yet knows how many near misses predict a crash.

Cautionary cases

A sepsis model that missed most sepsis

Tested independently on 38,455 hospital stays, it missed 67% of cases while alerting on 18% of patients. (JAMA Internal Medicine, 2021, A)

Test at your own site before switching it on.

Gunshot detection

In Chicago, 9.1% of dispatched alerts found evidence of a gun-related crime. (Chicago Inspector General, 2021, A)

Accurate detection isn't the same as public benefit.

Predicting who will be in a shooting

Chicago's list didn't change listed people's chances of being shot, but they were more likely to be arrested. (Journal of Experimental Criminology, 2016, A)

Predicting what someone might do is the opposite of recording what they did.

AI wildfire sensors

Federal auditors counted misses and late alerts. At one site, 9 of 13 alerts were false. (DHS Inspector General, 2026, A)

Count every miss, not the highlight reel.

What the cases have in common

  • The best uses give the AI a narrow job with an answer someone can check: an eye grade, a river gauge, a methane plume.
  • A cheap, helpful action follows the alert, like an eye exam or a second look at a patient. Coercive actions, like a police stop, raise the stakes of every error.
  • Things go wrong in the rules around the AI more often than in the AI itself.
  • Most of the positive studies come from the people who build the tools. Most of the failures were found by inspectors general and outside researchers. Independent checking is the missing piece in almost every case.

Know a good use?

Seen AI do a small job that made life better, or one that went wrong? Tell me about it. I'll look into it, and if it holds up, it goes on this page.

Email me

dawiseman21@gmail.com