Research? Sure. Accurate? Yeah, Sort Of

October 19, 2021

Facebook is currently under scrutiny unlike any it has seen since the 2018 Cambridge Analytica scandal. Ironically, much of the criticism cites research produced by the company itself. The Verge discusses “Why These Facebook Research Scandals Are Different.” Reporter Casey Newton tells us about a series of stories about Facebook published by The Wall Street Journal collectively known as The Facebook Files. We learn:

“The stories detail an opaque, separate system of government for elite users known as XCheck; provide evidence that Instagram can be harmful to a significant percentage of teenage girls; and reveal that entire political parties have changed their policies in response to changes in the News Feed algorithm. The stories also uncovered massive inequality in how Facebook moderates content in foreign countries compared to the investment it has made in the United States. The stories have galvanized public attention, and members of Congress have announced a probe. And scrutiny is growing as reporters at other outlets contribute material of their own. For instance: MIT Technology Review found that despite Facebook’s significant investment in security, by October 2019, Eastern European troll farms reached 140 million people a month with propaganda — and 75 percent of those users saw it not because they followed a page but because Facebook’s recommendation engine served it to them. ProPublica investigated Facebook Marketplace and found thousands of fake accounts participating in a wide variety of scams. The New York Times revealed that Facebook has sought to improve its reputation in part by pumping pro-Facebook stories into the News Feed, an effort known as ‘Project Amplify.’”

Yes, Facebook is doing everything it can to convince people it is a force for good despite the negative press. This includes implementing “Project Amplify” on its own platform to persuade users its reputation is actually good, despite what they may have heard elsewhere. Pay no attention to the man behind the curtain. We learn the company may also stop producing in-house research that reveals its own harmful nature. Not surprising, though Newton argues Facebook should do more research, not less—transparency would help build trust, he says. Somehow we doubt the company will take that advice.

A legacy of the Cambridge Analytica affair is the concept that social media algorithms, perhaps Facebook’s especially, is reshaping society. And not in a good way. We are still unclear how and to what extent each social media company works to curtail false and harmful content. Is Facebook finally facing a reckoning, and will it eventually extend to social media in general? See the article for more discussion.

Cynthia Murrell October 19, 2021

Money Put to Good Use at MIT

September 22, 2021

The Massachusetts Institute of Technology had a brush with Mr. Epstein, who continues to haunt the “real news” due to that estimable royal, Prince Andrew. And what of the institution which found Mr. Epstein amiable and enthusiastic about education and research?

The MIT experts have published absolutely stunning data about driver-assist technology. “A Model for Naturalistic Glance Behavior around Tesla Autopilot Disengagements” is a title crafted with the skill of the MIT professionals who explained MIT’s interactions with Mr. Epstein.

What’s fascinating is one conclusion from this official research paper, which MIT will sell to a person eager to support this outstanding institution. Here’s the finding I circled:

Visual behavior patterns change before and after AP disengagement. Before disengagement, drivers looked less on road and focused more on non-driving related areas compared to after the transition to manual driving. The higher proportion of off-road glances before disengagement to manual driving were not compensated by longer glances ahead.

What’s this mean to a person in rural Kentucky? Vehicles which “sort of drive themselves” make drivers fiddle with their phones and do stuff not associated with paying attention to driving.

Who knew?

Stephen E Arnold, September 22, 2021

Is Pew Defining News Too Narrowly?

September 21, 2021

I read what looks like another “close enough for horse shoes survey.” The data originate from the Pew Research Center, which has adopted the role of the outfit which says, “This is what’s shaking the digital world.”

The article “News Consumption across Social Media in 2021” reports that ”about half of Americans get news on social media at least sometimes, down slightly form 2020.”

But what’s news? I don’t want to dive into the definitional quandary, but news? What’s truth? Ethical behavior? Honor?

There is a factoid tucked into the write up which is interesting because it seems that hot social media properties like Reddit, TikTok, LinkedIn (Microsoft), Snapchat, WhatsApp, and Twitch are not where Americans go for news.


Let’s zoom into Reddit. The majority of the content is news related; that is, the information calls attention to an action or instrumentality. One easy example is the discussion threads related to problems with computers. Isn’t this information news?

What about WhatsApp (Facebook)? With encrypted messaging services becoming the new Dark Web, much of the information on special interest groups focused on possible illegal activities is, according to my DarkCyber research team, is news: Who, what, where, when, etc.

Another issue is that anyone with an interest in an event (for instance, a law enforcement professional) may find quite “newsy” items on Facebook and YouTube pages. And the sampling used for the Pew study? Maybe not representative?

Net net: Interesting study just a slight shading of “news.” The world has changed and as cartoon characters once said, “Phew, phew.”

Stephen E Arnold, September 21, 2021

Smart Software: Boiling Down to a Binary Decision?

September 9, 2021

I read a write up which contained a nuance which is pretty much a zero or a one; that is, a binary decision. The article is “Amid a Pandemic, a Health Care Algorithm Shows Promise and Peril.” Okay, good news and bad news. The subtitle introduces the transparency issue:

A machine learning-based score designed to aid triage decisions is gaining in popularity — but lacking in transparency.

The good news? A zippy name: The Deterioration Index. I like it.

The idea is that some proprietary smart software includes explicit black boxes. The vendor identifies the basics of the method, but does not disclose the “componentized” or “containerized” features. The analogy I use in my lectures is that no one pays attention to a resistor; it just does its job. Move on.

The write up explains:

The use of algorithms to support clinical decision making isn’t new. But historically, these tools have been put into use only after a rigorous peer review of the raw data and statistical analyses used to develop them. Epic’s Deterioration Index, on the other hand, remains proprietary despite its widespread deployment. Although physicians are provided with a list of the variables used to calculate the index and a rough estimate of each variable’s impact on the score, we aren’t allowed under the hood to evaluate the raw data and calculations.

From my point of view this is now becoming a standard smart software practice. In fact, when I think of “black boxes” I conjure an image of Stanford University and the University of Washington professors, graduate students, and Google-AI types which share these outfits’ DNA. Keep the mushrooms in the cave, not out in the sun’s brilliance. I could be wrong, of course, but I think this write up touches upon what may be a matter that some want to forget.

And what is this marginalized issue?

I call it the Timnit Gebru syndrome. A tiny issue buried deep in a data set or method assumed to be A-Okay may not be. What’s the fix? An ostrich-type reaction, a chuckle from someone with droit de seigneur? Moving forward because regulators and newly-minted government initiatives designed to examine bias in AI are moving with pre-Internet speed?

I think this article provides an interest case example about zeros and ones. Where’s the judgment? In a black box? Embedded and out of reach.

Stephen E Arnold, September 9, 2021

Techno-Psych: Perception, Remembering a First Date, and Money

September 9, 2021

Navigate to “Investor Memory of Past Performance Is Positively Biased and Predicts Overconfidence.” Download the PDF of the complete technical paper at this link. What will you find? Scientific verification of a truism; specifically, people remember good times and embellish those memory with sprinkles.

The write up explains:

First, we find that investors’ memories for past performance are positively biased. They tend to recall returns as better than achieved and are more likely to recall winners than losers. No published paper has shown these effects with investors. Second, we find that these positive memory biases are associated with overconfidence and trading frequency. Third, we validated a new methodology for reducing overconfidence and trading frequency by exposing investors to their past returns.

The issue at hand is investors who know they are financial poobahs. Mix this distortion of reality with technology and what does one get? My answer to this question is, “NFTs for burned Banksy art.”

The best line in the academic study, in my view, is:

Overconfidence is hazardous to your wealth.

Who knew? My answer is the 2004 paper called “Overconfidence and the Big Five.” I also think my 89-year-old great grandmother who told me when I was 13, “Don’t be over confident.”

I wonder if the Facebook artificial intelligence wizards were a bit too overconfident in the company’s smart software. There was, if I recall, a question about metatagging a human as a gorilla.

Stephen E Arnold, September 9, 2021

Not an Onion Report: Handwaving about Swizzled Data

August 24, 2021

I read at the suggestion of a friend “These Data Are Not Just Excessively Similar. They Are Impossibly Similar.” At first glance, I thought the write up was a column in an Onion-type of publication. Nope, someone copied the same data set and pasted it into itself.

Here’s what the write up says:

The paper’s Excel spreadsheet of the source data indicated mathematical malfeasance.

Malfeasance. Okay.

But what caught my interest was the inclusion of this name: Dan Ariley. If this is the Dan Ariely who wrote these books, that fact alone is suggestive. If it is a different person, then we are dealing with routine data dumbness or data dishonesty.


The write up contains what I call academic ducking and covering. You may enjoy this game, but I find it boring. Non reproducible results, swizzled data, and massaged numerical recipes are the status quo.

Is there a fix? Nope, not as long as most people cannot make change or add up the cost of items in a grocery basket. Smart software depends on data. And if those data are like those referenced in this Metafilter article, well. Excitement.

Stephen E Arnold, August 24, 2021

Apple: Change Is a Constant in the Digital Orchard

August 18, 2021

Do you remember how plans would come together at the last minute when you were in high school. Once the gaggle met up, plans would change again. I do. Who knew what was going on? When my parents asked me, “Where are you going?” I answered directly: “I don’t know yet.”

Apple sparked a moment of déjà vu for me when I read “Apple Alters Planned New System for Detecting Child Sex Abuse Images over Privacy Concerns.” The write up explained that the high school science club member have allowed events to shape their plans.

Even more interesting is what the new course of action will be; to wit:

The tech giant has said the system will now only hunt for images that have been flagged by clearinghouses in multiple countries.

How’s this going to work? Mode, median, mean, row vector value smoothing, other? The write up states:

Apple had declined to say how many matched images on a phone or a computer it would take before the operating system notifies them for a human review and possible reporting to authorities.

Being infused with the teen aged high school science club approach to decision making, some give the impression of being confused or disassociated from the less intelligent herd.

I have some questions about how these “clearinghouses in multiple countries” will become part of the Apple method. But as interested as I am in who gets to provide inputs, I am more interested in those thresholds and algorithms.

I don’t have to worry, one of the Apple science club managers apparently believes that the core of the system will return 99 percent or greater accuracy.

That’s pretty accurate because that’s six sigma territory for digital content in digital content land. Amazing.

But that’s the teen spirit which made high school science club decisions about what to do to prank the administrators so much fun. What happens if one chows down on too many digital apples? Oh, oh.

Stephen E Arnold, August 18, 2021

Why Some Outputs from Smart Software Are Wonky

July 26, 2021

Some models work like a champ. Utility rate models are reasonably reliable. When it is hot, use of electricity goes up. Rates are then “adjusted.” Perfect. Other models are less solid; for example, Bayesian systems which are not checked every hour or large neural nets which are “assumed” to be honking along like a well-ordered flight of geese. Why do I offer such Negative Ned observations? Experience for one thing and the nifty little concepts tossed out by Ben Kuhn, a Twitter persona. You can locate this string of observations at this link. Well, you could as of July 26, 2021, at 630 am US Eastern time. Here’s a selection of what are apparently the highlights of Mr. Kuhn’s conversation with “a former roommate.” That’s provenance enough for me.

Item One:

Most big number theory results are apparently 50-100 page papers where deeply understanding them is ~as hard as a semester-long course. Because of this, ~nobody has time to understand all the results they use—instead they “black-box” many of them without deeply understanding.

Could this be true? How could newly minted, be an expert with our $40 online course, create professionals who use models packaged in downloadable and easy to plug in modules be unfamiliar with the inner workings of said bundles of brilliance? Impossible? Really?

Item Two:

A lot of number theory is figuring out how to stitch together many different such black boxes to get some new big result. Roommate described this as “flailing around” but also highly effective and endorsed my analogy to copy-pasting code from many different Stack Overflow answers.

Oh, come on. Flailing around. Do developers flail or do they “trust” the outfits who pretend to know how some multi-layered systems work. Fiddling with assumptions, thresholds, and (close your ears) the data themselves  are never, ever a way to work around a glitch.

Item Three

Roommate told a story of using a technique to calculate a number and having a high-powered prof go “wow, I didn’t know you could actually do that”

No kidding? That’s impossible in general, and that expression would never be uttered at Amazon-, Facebook-, and Google-type operations, would it?

Will Mr. Kuhn be banned for heresy. [Keep in mind how Wikipedia defines this term: “is any belief or theory that is strongly at variance with established beliefs or customs, in particular the accepted beliefs of a church or religious organization.”] Just repeating an idea once would warrant a close encounter with an Iron Maiden or a pile of firewood. Probably not today. Someone might emit a slightly critical tweet, however.

Stephen E Arnold, July 26, 2021

A Google Survey: The Cloud Has Headroom

June 17, 2021

Google sponsored a study. You can read it here. There’s a summary of the report in “Manufacturers Allocate One Third of Overall IT Spend to AI, Survey Shows.”

First, the methodology is presented on the final page of the report. Here’s a snippet:

The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 to November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country was weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.

Google apparently wants to make data a singular noun. That’s Googley. Also, there are two references to weighting; however, there are no data for how the weighting factors were calculated nor why the weighting factors were need for what boils down to a set of countries representing the developed world. I did not spot any information about the actual selection process; for example, mailing out a request to a larger set and then taking those who self select is a practice I have encountered in the past. Was that the method in use here? How much back and forth was there between the Harris unit and the Google managers prior to the crafting of the final report? Does this happen? Sure, those who pay want a flash report and then want to “talk about” the data. Is it possible weighting factors were used to make the numbers flow? I don’t know. The study was conducted in the depths of the Covid crisis. Was that a factor? Were those in the sample producing revenue from their AI infused investments? Sorry, no data available.

What were the findings?

Surprise, surprise. Artificial intelligence is a hot button in the manufacturing sector. Those who are into smart software are spending a hefty chunk of their “spend” budget for it. If that AI is delivered from the cloud, then bingo, the headroom for growth is darned good.

The bad news is that two thirds of those in the sample are into AI already. The big tech sharks will be swarming to upsell those early adopters and compete ferociously for the remaining one third who have yet to get the message that AI is a big deal.

Guess what countries are leaders in AI. If you said China, wrong. Go for Italy and Germany. The US was in the middle of the pack. The laggards were Japan and Korea. And China? Hey, sorry, I did not see those data in the report. My bad.

Interesting stuff in these sponsored research projects with unexplained weightings which line up with what the Google says it is doing really well.

Stephen E Arnold, June 17, 2021

Search Share, Anyone? Qwant, Swisscows, Yandex, Yippy? (Oh, Sorry, Yippy May Be a Goner)

May 17, 2021

A recent study by marketing firm Adam & Eve DDB examined the impact of search-result placement on brand visibility over the past six years. McLellan Marketing Group summarizes the findings in it’s post, “Share of Search.” A company’s “share of search” is the percentage of searches for its product category that result in its site popping up near the top. The Google Analytics dashboard helpfully displays organizations’ referrals for specific keywords and phrases, while the Google Keyword Tool reports overall searches for each term or phrase. The study checked out the metrics for three examples. We learn:

“[Adam & Eve DDB’s Les] Binet explored three categories: an expensive considered purchase (automotive), a commodity (gas and electricity) and a lower-priced but very crowded brand segment (mobile phone handsets). The results were very telling. Here are some of the biggest takeaways:

Share of search correlates with market share in all three categories.

Share of search is a leading indicator/predictor of share of market – when share of search goes up, share of market tends to go up, and when share of search goes down, share of market falls.

This long-term prediction can also act as an early warning system for brands in terms of their market share.

Share of voice (advertising) has two effects on share of search: a significant short-term impact that produces a big burst but then fades rapidly, and a smaller, longer-term effect that lingers for a very long time.

The long-term effects build on each other, sustaining and growing over time.

Share of search could also be a new measure for brand strength or health of a brand by measuring the base level of share of search without advertising.

While share of search provides essential quantitative data, brands should also use qualitative research and sentiment analysis to get a more robust picture.”

We are told that when a brand’s search share surpasses its market share, growth is on the way. Yippee! How can one ensure such a result? Writer Drew McLellan reminds us that relevant content tailored to one’s audience is the key to organic search performance. Or one could just take the shortcut: buying Facebook and Google ads also does the trick. But we wonder—where is the fun in that? Yippy? Yippy? Duck Ducking the search thing?

Cynthia Murrell, May 17, 2021

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