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

What's wrong with this data?

I've talked about "fake data" before. A lot of fake data come from people trying to game algorithms or skew metrics, and oftentimes, automated bots are involved. Attempts to obscure these tactics typically involve creating layers of complexity so it is not easy to connect the dots.

I come across suspicious data all the time, and it's not always clear what's going on or why. So I thought I'd feature some of them here and see if anyone can figure it out.

***

This account on Yelp caught my attention because this user apparently uploaded a photo of her desktop to one of the restaurant pages. She has uploaded a total of four photos, all of them are unrelated to food. The four photos were uploaded to two New York City restaurants while she indicated that she lives in San Francisco. She did not review either of those NYC restaurants but she has written one review for a cafe in Long Island City. The review seems genuine (although it's hard to tell unless you've been to that cafe).

She has five friends. While she lives in San Francisco, these friends live in Manhattan, Brooklyn, Scottsdale and Oceanside. She has no friends in the Bay Area. None of these friends have ever written a single review and have no likes. However, each of these friends have 100-400 friends. It's not clear why one would be friends with someone on Yelp who has no reviews or likes.

***

Is this account fake? If so, why was it created? How did those photos get uploaded? How did they get placed in those particular restaurants? Who are these friends? Are they fake as well? If the account is fake, was that review also fake? Is it possible to predict that the review is fake?

So many questions, and so hard to get answers. What do you think is going on?

 

 

More Grubhub theatrics, with Yelp as supporting actor

Just recently, I made a short clip about how Grubhub is extracting lead-generation fees from unsuspecting restaurant owners by setting up a network of shadow websites (and phone numbers). Diners who thought they were ordering directly from restaurants were instead shuffled through the Grubhub toll booth. Click here to see how this works.

 

 

Now, Vice discovered yet another of Grubhub's toll booths. This time, the toll booth is set up on the Yelp superhighway. When a Yelp user clicks on the restaurant's phone number on its Yelp page, a pop-up shows up, with two options, one of which redirects the user to a shadow number owned by Grubhub. These numbers are not labelled "direct" and "Grubhub" but "general questions" and "deliveries and orders". So now both Yelp and Grubhub are making lead-generation money off the restaurant owner.

***

This dispute is about causality! And because causality is tough to establish, it creates a gray zone of disagreement.

Ideally, the restaurant owner pays for orders caused by Grubhub's marketing activities. By cause, we mean the orders would not have materialized without Grubhub's marketing. In the examples we saw, it's highly unlikely that Grubhub did anything to cause those orders. That's because those diners thought they were ordering directly from the restaurants - they are quietly re-routed to the shadow websites and phone numbers set up by Grubhub, sometimes without even the restaurants' knowledge.

It is the secrecy that gives the game away. If Grubhub's causal value is clear, it can form open partnerships with the restaurants without resorting to trickery.

As I pointed out in the video, the majority of the digital marketing industry relies on similar tactics. Search engines do not typically send you directly to the webpage you clicked on - you are often rerouted through the search engine's server, so that the search engine can "track" the click and use the paper trail to receive lead-generation money. Anyone passing through this toll booth is counted as "causal" but in reality, many of these users would have found their way to the webpage even if the search engine didn't exist. (Consider going directly to Macys.com instead of typing Macys into a search engine.)

***

This is a great example of how data, algorithms and software are silently running our lives, and often to the detriment of those who don't understand what's going on. Our video series is a small effort to help you stay in front of these data-driven technologies.

The more you know, the more you can leverage its power, and avoid its harms.

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.

 

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.

***

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."

***

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.

***

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.

The almighty advertising dollar

Writing for Fast Company, Richard Stokes, a reformed advertising executive now running a privacy consultancy, explained why he left the digital advertising industry (link).

It's about the broken contract between advertisers and consumers - with "adtech" guilty of causing the split. Here's the key paragraph:

Advertising had ceased to be about connecting with consumers—it was now about finding novel ways of extracting evermore personal information from computers, phones, and smart homes. To many of the most powerful and profitable companies in the world, we are the products, and the services we all use are just afterthoughts they put out to keep us hooked. And the rest of the ad industry, which depends on their data to compete, has no choice but to go along with whatever whims and changes come their way.

It's sad but true that "the services we all use are just afterthoughts". Google, Facebook, Yelp, etc. make it impossible for users to contact them in any channel. They justify these policies because users aren't paying them, only advertisers do.

The latest fracas around hate speech and bullying on Youtube underlines this point: even as management accepted that the Youtuber violated community standards, they deemed the appropriate penalty to be "de-monetize," i.e. disallow the Youtuber from making money off advertisements in those videos -- instead of removing the offensive clips. The almighty advertising dollar: that's their paramount concern.

Richard proposes several steps to rebuild trust:

• Obtain consent before collecting and sharing data
• Stop refusing service if user does not consent
• Provide full disclosure of where the data go
• Never collect certain types of data, such as health-related, location-related

This list has some overlaps with my list of 7 Principles of Responsible Data Collection (link).

***

For those in New York, Principal Analytics Prep is hosting an interactive workshop next Tuesday on digital ad fraud "hunting". Augustine Fou, an independent researcher on ad fraud, will lead the session and we'll use analytics to find fraud in advertising data.

To register for the workshop, go to our Meetup page.

To apply for our Summer 2019 bootcamp, go here.

 

 

Losing control of the data

Another day, another story about Facebook data. Ars Technica reports that Facebook is suing a South Korean app developer Rankwave, claiming the company misused data it received from Facebook. Rankwave creates mobile apps through which it obtained Facebook user data for 10 years.

All we know about this situation came from the Facebook press release, and it's not clear what the offense is. The article cited the violation as using the Facebook data to "create and sell advertising and marketing analytics and models". That's how Facebook uses the user data, same as why Facebook partners want access to user data.

One part of the press release rings very true: Facebook admits that it does not control the data once shared with third parties. Facebook lawyers demanded Rankwave do the following:

• Provide a full accounting of Facebook user data in its possession;
• Identify all individuals, organizations, and governmental entities to which it had sold, or otherwise distributed, Facebook user data;
• Provide a full record of the access logs and permissions it had granted third parties to access the data;
• Delete and destroy all Facebook user data after returning it to Facebook;
• Provide Facebook with full access to all storage and related devices so that Facebook could confirm deletion and destruction of the data through an audit.

These all sound great but would any company, even Facebook, be able to deliver the above logs, reports, devices, etc.? Given how data are spread out in big networks of servers ("data clouds", "data lakes", etc.), this wishlist sounds like a fantasy.

In this new video, I talk about the data sharing ecosystem, why it is so hard to delete anything, and how companies lose control of the data. It's the flip side of speed and convenience. What is the price we're willing to pay?

Click here to see all my videos.

Labeling data: a crucial part of machine learning

Several recent news stories cover the topic of “labeling” data. For example, this Bloomberg article says Amazon is sending voice recordings from its Echo speakers to be heard and transcribed by human listeners. This Reuters article discusses Facebook’s contractors in India, Romania, Philippines, etc. hired to “label” status updates, shared links, event posts, Stories feature uploads, videos and photos.

The authors of these articles express genuine shock and awe. They apparently believed that “machine learning” means no humans involved. The tech industry allows this misconception to fester by being opaque about how machine learning works.

(The reporters are also dismayed by the privacy invasion. The Echo speakers are constantly recording in users’ households. Facebook did not have explicit permission from users to it send their data out for labeling.)

***

Humans have always been a part of the machine learning workflow, and will continue to be. Let’s use one of the examples in the Facebook report to illustrate this point.

A machine learning model predicts profane language in a Facebook video. For the time being, I assume that a predictive model already exists. This model is being fed videos, and automatically makes predictions - without human involvement. Video 1 – it predicts profanity; Video 2 – no; 3 – no; 4 – yes; etc.

Computers work fast, and can make tons of predictions quickly. The question is whether these predictions are accurate. It’s one thing to create these models in the laboratory; it’s another thing when they are unleashed to the world, and affecting Facebook users, e.g. by deleting videos that are predicted to contain profanity.

Why should Facebook and its data scientists care about predictive accuracy?

User complaints. When users find their videos deleted due to profanity, they complain if said clips do not in fact contain profane language. Other users are upset to unsuspectingly encounter videos with profanity (that the machine fails to identify and delete).

***

It’s not easy to measure if the machine-learning model is correctly predicting profanity. The machine can’t be both decider and judge at the same time. The judge typically is a human who views the video to determine if it contains profanity. These human judges are the “annotators” described in the news articles. They are hired to look through videos and apply a profanity “label” if they find profanity.

As disclosed in the articles, companies typically hire two or three judges for each item because profanity is a somewhat subjective opinion. They might order more detailed labeling, e.g. label types of profanity as opposed to one overall label of profanity.

***

Now let’s remove the assumption that we already have a machine learning model. Where does this model come from? Such a machine has to know what features of the video are correlated with presence of profanity. To discover this correlation, the machine needs to be told which videos have profane language, and which do not.

This is a chicken-and-egg problem and it is solved by having humans label a big batch of videos at the start, building the “training data”. In the Facebook example, they hired over 200 people to create data labels, later reduced to 30. The first team was building a large training dataset; after the predictive model has been produced, the future labeling by the reduced workforce is used to monitor the accuracy of the predictions.

Any company that claims to use our data to predict our behavior must create training data, i.e. labeled data. In most cases, humans must create the labels – by reading our emails, listening to our conversations, viewing our videos, reviewing our calendars, scanning our receipts, and so on.

How far companies should go and what methods they shoudl use in collecting such data are ethics questions that should be discussed.

It's not us; it's the weather

If you are a frequent flier, you already know the gist of this nice article by the BBC: that airlines are allowed to sandbag the flight durations. A flight that takes 60 minutes will be portrayed to fliers as taking twice as long, if not longer.

The airlines are even allowed to lie about this practice. When your flight is delayed taking off, the captain claims that s/he will “make up for the delay,” as if the plane could be driven faster on command. (Were they deliberately going slower before?) The truth is that the schedule is padded, so that it can absorb a limited amount of delay.

This quote sums the situation up: “By padding, airlines are gaming the system to fool you.”

At the very bottom of the article, you’d find the potential motivation – to avoid compensating travelers for long delays, as required by law in some countries.

***

The situation here is similar to the road congestion problem discussed in Chapter 1 of Numbers Rule Your World (link). Managing perceived time is as important as managing actual time experienced by the traveler. Of course, reducing actual wasted time is preferred, especially to scientists working on the problem, but when the road/sky capacity is fixed and over-subscribed, it’s almost impossible to attain. The second half of the article addresses “why don’t the airlines work on efficiency instead of lengthening flight times?”

***

Another quote reveals: “Over 30% of all flights arrive more than 15 minutes late every day despite padding.”

The “on-time arrival rate” blessed by the Department of Transportation (DoT) is not what you think it is.

Let’s take a random flight that takes 60 minutes. This flight schedule might be padded and advertised as departing at 2 pm and arriving at 4 pm.

If the flight departs at 2 pm and takes 60 minutes, then you’d think on-time arrival is defined as arriving at 3 pm. You might agree to allow for some slack, say, 15 minutes. In this case, on-time arrival is arriving before 3:15 pm.

Given the discussion, you now know that on-time arrival is actually arriving before 4 pm since the schedule is padded not by 15 minutes but by 60 minutes.

And then you’d be wrong! Because there is padding upon padding. Airlines are allowed to claim “on-time arrival” if the flights arrive within 15 minutes of the scheduled arrival time, which in our example, has been padded by 60 minutes. So any flight arriving before 4:15 pm is counted as “on-time.”

***

Padding is not purely a bad thing. A certain amount of padding is necessary because lots of flights are vying for a limited amount of airport and air space. A padded schedule is a more accurate schedule. It acknowledges other factors that cause delays in arrival.

The gaming of the padded metric is what works people up. Gaming is possible because padding inserts subjectivity into the measurement. So long as subjectivity cannot be avoided, gaming is here to stay.

***

The reporter said airlines have spent billions on technologies to improve efficiency, i.e., managing actual experienced time. But, “this has not moved the needle on delays, which are stubbornly stuck at 30%.”

Now square that statement with this one: “Billions of dollars in investment [in modernising air traffic control] have in fact halved air traffic control-caused delays since 2007 while airline-caused delays have soared.”

Does this sound like the new technologies have successfully reclassified delays from air traffic control caused to airline caused? Passing the buck?

The next time your flight is delayed, the airlines will likely tell you, “it’s not us, it’s the weather.”

 

 

Is A/B testing that scary?

Reader AR pointed me to this Fast Company article that examines the ethics of A/B testing.

The only way to comprehend this point of view is to think of A/B testing not as a scientific experiment but as a decision-making process that involves running an experiment. The researchers are unhappy that A/B tests could lend support to decisions that have undesirable impact on society.

Two such examples are described:

• Two images are tested for a job ad. During the test, site visitors were shown one of the two images, selected at random. The winner of the test is an image that disproportionately drives male applicants.
• Separate pricing tests are run in different zip codes. The "winning" prices at the conclusion of these tests are different for different zip codes. Because racial profiles differ by zip code, prices are in effect different for different races. Therefore, the test result leads to race-based discrimination.

There are two important questions to discuss here. First, what is the alternative to A/B testing? Is that method of decision-making better? Second, is the harm produced by the experiment itself, or by the decision made as a result?

 

Alternatives to A/B Testing

Consider the image test described above. Presumably, the test is run because someone believes that one of those two images might perform better at driving applicants. At most companies, a test sees the light of day after teams of people debate and prioritize testing ideas. If the test including a sexist image is run, then the team in charge of testing has approved it for some (possibly bad) reason.

If they didn't have A/B testing, how would they have decided which image to run? And if the image is not explicitly sexist - in other words, if the analyst had to analyze the data to learn that one image drove more male applicants - how would that insight be surfaced without running the other image? The alternative decision process may be even worse.

It is certainly true that automated A/B testing is risky - because no human beings are involved in turning test results into actions. The absence of humans is usually touted as a benefit by vendors of such testing tools. In this example, a human analyst reports on the test result, and includes the analysis by gender showing that while total applications increased, the winning image disproportionately attracted male applicants. The decision-makers can and should decide not to adopt the winning image based on that analysis. The A/B test revealed the bias but did not cause it.

Even without the gender issue, such analysis and discussion of results is necessary. For example, ad clicks can be generated by placing ads near scroll bars to stimulate accidental clicking. Human analysts can report that clicks increased but only through accidental clicking. The decision-makers can and should decide not to implement the winning design.

From where does the harm come?

The other example is more far-fetched. I am reverse-engineering the pricing test as described. Given that the test led to different prices for different zip codes, they would be running separate A/B tests stratified by zip code. Given the law of supply and demand, it might be the case that the winning price would be lower in poorer zip codes and higher in richer zip codes. This definitely results in price discrimination by zip code. If the design team did not want price discrimination by zip code, then such a test design would not have been approved so the test itself isn't creating harm.

Further, race-based price discrimination is accused because zip codes are correlated with race. Almost all variables are correlated with race. Age is correlated with race, so are income, education, what websites one visits, etc. So this standard leads to a banning of all segmentation and targeting policies. The only possible pricing policy would be one price for all.

***

In short, human supervision of A/B testing from design to interpretation is definitely needed. A/B tests provide a wealth of data to support decision-making. The biases highlighted by the Fast Company article are merely revealed by the testing - they are not caused by it.