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

Proving to a machine that you are not a machine

We've all been stumped by those "Captcha" puzzles. They started with single words, then pairs of words, then single images, and now, panels of images. Sometimes, it's really hard to know what the answer is. The square that contains one tiny pixel of a traffic light, is that part of the traffic light or not?

It's human versus machine. Does the machine think that pixel is part of the traffic light or not? If you get it wrong, you will be punished by another set of nine images. If you fail multiple times, you will simply be locked out of the website you wanted to visit. Woe be you!

Imagine this. We are trying to prove to a machine that we are not machines! These Captcha puzzles are explicitly designed to keep bots away. The machine will decide whether you are a human being or not.

I explain this, and a lot more in my new Fung with Data video. This journey will take you through data science, big data, machine learning, deep learning, self-driving cars, and more. These things are all inter-connected with Captchas.

To see the video, click here.

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The Facebook data privacy settlement fails to impress

The FTC settled with Facebook on data privacy.

In the last year, news kept coming out about how the social media giant mishandled user data, including taking phone numbers collected for two-factor authentication and using them for marketing, saving passwords unencrypted, and asking users their passwords to other services. Despite the $5 billion fine, the settlement has been widely panned by the technology press as a slap on the wrist.

The size of the fine is not meaningful in the context of Facebook’s massive advertising business that generated $16 billion revenues in the 2^nd quarter of 2019 alone.

Even more frustrating are the so-called new regulations, which fail to address any underlying issues.

Consider the requirement that Facebook ask users to “opt-in” to facial recognition. Requiring opt-in has long lost any meaning because of the forced consent tactic. Facebook can simply put up a notice saying that you will not be served unless you consent to the following activities. This kind of notice is prevalent with mobile apps - to use these apps, you must agree to a list of demands.

Consider the requirement to encrypt passwords. This is standard IT best practice branded as regulatory success.

Consider the requirement to bar third-party apps that “fail to certify that they are in compliance with Facebook’s platform policies”. This is a reiteration of existing policy. The FTC does not stipulate any restriction on types of use. The issue is intractable since once the data moves to third-party servers, Facebook has no ability to monitor them.

As the following articles show, few are impressed by this settlement.

Business Insider: List of regulations

Vox: Facebook FTC settlement

Bloomberg: Privacy settlement won't hinder its ad business

Mashable: How FTC let Mark Zuckerberg off the hook

Techcrunch: 9 reasons the settlement is a joke

Needed: China concealers with smelling sushi

I got this transcribed voice message courtesy of Google:

I had a good laugh.

This is part of a larger pattern. I get a few of these every week. The other one from today said "Excellent Jim Shorkey. Yes." All of these incomprehensible transcriptions are from spam callers speaking in Mandarin Chinese.

What's happening is that the machine learning model that listens and transcribes the messages does not understand Chinese. It assumes everything is in English and applies the model to the incoming messages no matter what.

This is analogous to applying a linear regression model to data that are not linear. Your college professor might say "Look first before you run the regression." Here, it is "listen first before you run the transcriber."

Grubhub's lead generation tactics leave bitter taste with restaurant owners

In my new 3-minute video, I discuss the controversy about the revelation that Grubhub has been playing a dirty trick on its restaurant customers.

As I explain in the video, what Grubhub did is similar to what Google has been doing with its search engine.

Most of these online businesses make money off "lead generation" - they bring prospective customers to brands. Restaurant owners hope Grubhub will bring them additional diners. These have to be incremental diners to justify the expense, which is a sizable 20-percent fee Grubhub assesses for each lead.

Grubhub has every incentive to count each order as an incremental lead. The dirty trick involves counting orders who should not be charged the lead-generation fee. Grubhub sets up shadow restaurant websites with similar URLs that pretend to be official websites. The diner usually does not know s/he is ordering from Grubhub's website and not the official restaurant website. This bit of deception qualifies the order for the lead generation fee - if the same diner had ordered from the restaurant's own website, Grubhub would have collected a much lower fee.

Similarly, lots of us search for brand names on Google, instead of going directly to the brands' websites. This leads some brand managers to buy their own brand names from Google. Google over the years has made it harder and harder to see which result is a paid ad. If we click on those ads, the brands pay Google a lead generation fee. Nevertheless, if we are searching for a brand's name, we will be visiting that brand's website, whether or not we pass through the Google toll booth!

In Numbersense (link), I talk about the importance of counterfactual thinking, and measuring incremental rather than absolute metrics.

Click here to see the video about the Grubhub controversy.

 

Seven deaths due to walking between moving subway cars

MTA is running the following notice in the NY subway trains:

 

The person who wrote the line “7 people died while walking between moving subway cars” wants subway riders to think 7 is a lot, is too many. How we feel about this number depends on what we choose to compare it to. Seven out of 1.7 billion (MTA statistics) seems like a very small risk but if it’s 7 out of 100, it is very dangerous indeed.

The implied denominator can be the number of riders per year or the number of riders who attempt to walk between moving trains per year. Because of the availability heuristic (Kahneman & Tversky), most of us have a stronger sense of the first number than the second. But using the total riders as denominator causes us to vastly underestimate the risk. If the message had said “7 out of 100 people who walked between cars died”, that’s a strong deterrent against walking between cars.

Know your data 25: Roundup of the fast-and-furious news

The genie is out of the bottle. The press is now reporting, almost daily, new discoveries of all the unspoken things tech companies have been doing with personal data collected from our usage of their products and services.

First up, the reporter from Washington Post wondered how much surveillance is happening on the Chrome browser. He learned that the browser loaded 11 thousand trackers in just one week. Horrified, he dumped Chrome (a Google product) for Firefox.

Traditionally, the website you visit installs a cookie (a type of tracker) to remember you: when you visit a page, and it automatically remembers who you are, that's the classic use of a cookie. Almost all of the trackers nowadays are invited guests of the website you visit, third parties who just want to follow you around.

I have more to say about the surveillance industry in this video. It's not just Chrome that you have to worry about.

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When multiple websites like Facebook and Instagram temporarilly went down last week, news came out that Facebook automatically tags any photo you upload with "labels". During the downtime, the images did not load but the labels did. People saw things like "Photo may contain: two people, smiling, a beach". These are almost surely AI-created tags.

This is nothing new actually. In the past, you can see these labels if you are on a slow internet connection.

The real concern is the disclosure that Facebook automatically tags photos with people's names, using facial recognition technology. It appears that telling Facebook you don't want to be tagged means the tagging is moved underground, behind the scenes, out of your reach. I've been calling this the "illusion of control."

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Another instance of illusion of control came by way of Amazon, which admitted that audio recordings of Alexa users are retained even after these users delete their accounts and data.

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Next, a new email tool called Superhuman lets users to track whether the emails they sent have been opened by their recipients. This feature was on by default. This type of tracking uses "pixels" rather than cookies. A pixel is a tiny transparent "image" that is designed to hide from human eyes. When your browser pulls the pixel from its owner's server, you have made yourself known to that server.

This tracking feature has always been part of commercial email software. Whenever Amazon or your favorite brand sends you an email, analysts can tell when you open it, or interact with it. This data allow brands to assess the effectiveness of their emails.

Remember this the next time you pretend to your friend or colleague that you haven't seen his/her email.

 

Inside the Black Box: big-tech surveillance

Inside the Black Box is a series of short videos in which I explain how data, software and algorithms are influencing many aspects of our lives. Subscribe to the playlist here.

The fourth clip of this series takes off from this article by Washington Post columnist, Geoffrey Fowler, who announced that he has ditched the Chrome browser, switching to Firefox. Why did he do it? Should you?

[P.S. Oops. After posting this, I realized I haven't realized clip #3. So the big-tech surveillance will have to wait.]

The third clip of this series looks at the fall of the "depression gene". Twenty years ago, data analysts were sure they found a promising gene variant that is helpful to predict depression. Then it fell apart. How?

It's an important lesson for us all today as we dig into piles of observational data. There are a host of reasons for discoveries to not replicate from the lab to real life. One of them is the curse of dimensionality. I use the famous "birthday problem" to examine this issue.

We sure hope it doesn't take 20 years to realize one has made a false discovery!

Click here to see the clip.