Big Data, Plainly Spoken (aka Numbers Rule Your World)

Know your data 40: omnipresent listening

Apple markets itself as the tech giant that is most protective of user privacy. One reason to believe this claim is that unlike all the other tech companies, Apple charges premium prices for its products and is not as reliant on advertising as its revenue source. Nothing in this post disputes this assertion although we will wonder how low the bar is to quality as "most".

Apple recently settled out of court a lawsuit that alleges that Apple's Siri secretly records people's conversations, then extracts "insights" which are sold to advertising customers. It's a scenario that most of us recognize is possible, and perhaps many of us thought couldn't possibly be happening, not on an Apple device.

The allegation is that some users were delivered "eerily accurate targeted ads that appeared after they had just been talking about specific items like Air Jordans or brands like Olive Garden".

***

Of course, any interactive technology requires the tech to capture the user's instructions or reactions and thus we expect the tech to be recording, whether it's audio or video. This is true of Siri, other voice assistants, any home devices you can interact with, gaming and TV monitors, etc.

So, if one chooses to use these features, one should expect omnipresent listening.

Just think about the idea that "Hey, Siri" is used to activate Siri. If the phone is not constantly listening to you, how does it pick up "Hey, Siri"?

The concern is really about the lack of user control, or the disrespect of user intention (what has been euphemistically called "unintentional" here). I laugh every time the word "unintentional" is used. There is almost nothing that is unintentional in tech. The recording has to be triggered by some instruction and that instruction is written up in the code, a deliberate act. It's possible that the recording is set to be universal regardless of user intention or controls but that's not what is being admitted.

The allegation is that Siri was supposed to record conversations only if the user utters the words "Hey Siri" but in fact, conversations were (sometimes) recorded even when those words were not spoken.

Further, one doesn't typically hand over entire recordings to advertisers. The recordings must be turned into transcripts, then insights are extracted (e.g. person A mentioned brand X), the person A has to be identified so that the advertiser can send ads to that person's devices, etc.

The amount of code needed to execute the entire targeted advertising program, starting with recording the conversations and ending with delivering the ads, is substantial and involved. In my view, if such code exists, it is intentional.

***

It just so happens that while I was writing this up, a friend called me up and told me that his Instagram (that's a Facebook property) started flooding him with ads from a particular VPN brand after he recently asked me for VPN recommendations. He was quite alarmed and annoyed. He said he only wrote down the recommended names on a notepad so the only way Instagram knows we talked about that vendor would be through listening to our phone call.

That scenario is certainly possible but hard to prove since there is so little transparency when it comes to technology. In fact, any number of trackers may be collecting the data, which eventually find their way to Instagram.

Without whistleblowers or discovery, it's challenging to prove the case against Apple in court. But since Apple is settling, we might not hear about this again.

 

 

On the second day of congestion pricing...

Here comes the most predictable story of the AI revolution in toll collection. Drivers are evading tolls, stupid! (link)

Since Sunday, New York City has imposed tolls for driving into midtown and downtown Manhattan, which can potentially reduce the volume of traffic, and thus pollution in the city. They call it “congestion pricing,” which is a misnomer, since the price is not zero when there is zero congestion (the toll is $2.25 for passenger cars after 9 pm).

In any case, "congestion pricing" is powered by omnipresent surveillance, as AI image recognition technology is used to recognize car license plates, and then via a database lookup to establish their identities, the car owners are sent bills. You need lots of cameras all over the city to make this possible. According to this report (link), they installed 1,400 cameras in 110 “detection points”.

It used to be that cars lined up to pass by toll booths where each driver must hand over cash to the human toll collector before the car was allowed to pass through. This means drivers paid first, then used the roads. Then came AI image recognition technology, which snaps photos of cars, extracting the license plate numbers, and via a database lookup, sends bills to people’s homes. As a result, drivers get to use the toll roads first, and pay later.

It doesn’t take a genius to figure out that one can’t get charged if the AI failed to discover one’s license plate number. One can use any number of ways to obscure the license plate, or not even show it. One can fake the plate numbers, or simply cover it up. It appears that the toll evasion problem is so serious that on the second day of congestion pricing, NYC officials have proposed new rules to crack down on such tactics.

The next thing they will discover is more revenue leakage – what proportion of plate numbers are incorrectly read, what proportion of bills are sent to the wrong addresses, and what proportion of people don’t pay the bills.

***

On a related matter, my own experience is that banks are trusting the handwriting recognition software used by ATMs to read checks less and less; I feel like more and more, I'm asked to enter the amount manually, which suggests that the software is not sure about the number, or that the banks are extra cautious. 

Was Debrina Kawam homeless?

The New York Times published a short entry on the life of one Debrina Kawam, the woman burned alive while sleeping in a New York subway car recently (link).

In the initial reporting, the victim was described as "homeless" but eventually, when she was identified, the police used language that suggests otherwise: they said she had a "brief stint" in the New York homeless shelter system. This CNN article even said:

The victim was initially believed to have been homeless, which complicated efforts to identify her, law enforcement sources previously told CNN.

The NYT reporter added details, saying:

In the fall of last year, Kawam was homeless. After an outreach team encountered her at Grand Central Terminal, she entered the New York City shelter system and was assigned to a facility in the Bronx. But she never showed up.

***

Since I have been digging into how the U.S. HUD (Department of Housing and Urban Development) counts the homeless population (here, here), Kawan's case presents an opportunity to put our knowledge to practice.

If she had been alive in late January, she would have been found by the homeless counting team to have been sleeping in the subway system, which means she is part of the "unsheltered" homeless. If she had registered with the assigned Bronx shelter, she would have been counted as "sheltered homeless."

Thus, the reporting that suggests she was not homeless at the time of her death appears inaccurate.

 

P.S. Another tidbit from the NYT article was her futile attempt to find her mother in New Jersey. If her mother had allowed her to stay even temporarily, Kawam would not be included in the homeless count (neither in New York nor in NJ) as this situation fits one of the exclusion criteria set out in the HUD Guide.

The science of counting more people using fewer people

In the previous post, I explored how the U.S. government counts the homeless population. We learn that they don’t try to count everyone year-round but estimate the count on a single day of the year. But they don’t really count every homeless person everywhere on one day, either. In this post, I look into how they handle the sampling dimension of geography.

Each jurisdiction (dubbed CoC in the HUD Guide) divides its territory into sub-regions. The sub-regions are labeled high- or low-density based on the anticipated number of homeless. The sub-regions are “sampled”: not all sub-regions are counted; those sub-regions without actual counts are estimated. All high-density sub-regions are chosen, plus a selection of low-density regions. This sampling scheme makes a lot of sense, as it expends the most effort in places where most homeless reside.

Elsewhere, the HUD Guide discloses that some regions are completely excluded, e.g. suburban sub-regions that have not reported any homeless people for a few years might be removed in subsequent years. Thus, there are really three sampling “strata”: high-density sub-regions are 100% sampled, zero-density sub-regions are 0% sampled, and the low-density sub-regions are partially sampled (the counts in those low-density areas that are not sampled will be subsequently estimated).

Unfortunately, the authors of the HUD Guide call the above sampling scheme a “Random Sample of Areas”. For example, on page 12, the guide misrepresents the sampling scheme as:

An example of a random geographic sample is a CoC [what I’ve been calling a jurisdiction] that selects some geographic areas (e.g. according to Census tracts or, city blocks) to represent the entire CoC geography. The data collected from the homeless people counted and interviewed in these regions would then be adjusted to represent the overall homeless population.

Without reading the details of the sampling scheme, one is misled to believe that every sub-region has equal chance of being selected, which isn’t an appropriate way of sampling these sub-regions, nor is this the recommended sampling scheme described later in the Guide.

Now, your numbersense might have detected something I slipped past you a few paragraphs ago... how is it that they know which sub-regions have high or low numbers of homeless before they start counting? Seems like there is a chicken-and-egg issue. This is indeed the case: in order to reduce the workload, we need to send more workers to high-density areas but if we know nothing about the homeless population, how can we designate some sub-regions as high-density?

The truth is any statistical procedure involves some guesswork. In this case, because HUD isn't dealing with homeless for the time time ever in 2024, they can use historical data as a guide; in addition, experts working at organizations that work daily with homeless people have valuable insights. Imagine a sub-region that has experienced a recent surge in homeless. The historical data would be misleading in this case but the expert knowledge would likely be informative.

***

Now, consider what happens inside a sub-region. We ask the same question as before: do we just walk around the entire area and count (and survey) every homeless person we encounter? In the HUD Guide, this method of counting is called “complete coverage count”.

Most large jurisdictions probably practice something called “known locations count” method. This involves compiling a list of all locations that homeless people tend to stay at overnight, and visiting those locations. This labor-saving tactic, just like the other one we’ve seen, carries the risk of under-counting. They also must work with experts to figure out all the spaces where the homeless reside.

The Guide also allows something called “service-based” count. This is another fascinating concept: the staff visits social service centers, such as food banks, that homeless people might visit during the day. This count is materially different from the other counts in two ways: the counting happens during the day (not at night); the counting does not happen on the night of the counting day but during a seven-day window that starts after the night of counting.

This "service-based" count is a remedial count; it is used to correct the initial night count. The assumption is that some homeless will be missed on the night of counting but they might show up at social service centers during the day. Deduplication is really important here. They must make sure the same person isn't counted twice. This is the reason why the service-based count doesn't commence until the main counting is finished.

***

If you still think counting people is easy, I'll leave you with one further complication.

Since the unsheltered homeless usually aren't found at their sleeping locations during the day, the counting takes place during the designated night, but the window of counting spans midnight. So, if left to our own devices, some of us will write in one date while the rest of us will write in the other date as the day of the count.

The Guide explicitly stipulates that every worker should use the date of the start of the counting window as the date of the count.

***

I was expecting the whole exercise of counting the homeless to be a mess but what I found instead was a well-written, quite comprehensive documentation of the methodology used by HUD workers to measure the homeless problem.