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August 19, 2019 by dratner Leave a Comment

Devil’s Dictionary of Tech

Every so often I see an homage to Ambrose Bierce’s original Devil’s Dictionary updated for the digital age. Since a dictionary can have many contributors, I’d like to offer some definitions of my own…

Privacy Policy: A legal agreement obfuscating how a company can take your data and keep it private from you.

Pinwheel: Trying to make colossal failure looking deliberate by making it witty or pretty. See also beachball, fail whale, oh snap.

App: software that gets between you and what you want to accomplish.

To Google Someone: See “peeping Tom” (arc).

Facebook Live: Indecent exposure.

User Generated Content: The wretched refuse of a mass of men leading lives of quiet desperation. Also ridden with plagiarism and misappropriation.

Anonymous: Unaccountable.

Crypto: The intersection of money laundering and mass speculation.

Scalable: A crime too big to be punished.

Disruptive: Breaking laws so quickly, brazenly, and massively that you get away with it for awhile.

Direct Message: A message meant only for you, the recipient, the platform, the NSA, and any interested advertisers.

Cookie: The bribe offered by would be abductors trying to get you into their van.

Internet Poll: Popularity contest.

Algorithmic Matching: popularity contest.

Internet Famous: Incredibly well known by 15 people for 15 seconds.

Infinite Scroll: what you get when you dip below the bottom of the barrel and just keep going down.

Ad Blocker: Free rider.

Peer-to-Peer: A technologically facilitating interaction between two people that somehow requires thousands of employees and billions of dollars in California.

Kambucha: A fermented beverage lacking alcohol but still useful for hazing or indoctrination. It smells like a gas leak, tastes like a swamp, and flows down the gullet with the consistency of sewage. A serving costs more that US minimum wage and it is de rigeur to offer free in all tech company cafeterias.

Uptime: The casino’s hours.

Two Screening: When you need a distraction from your distraction.

Texting: When you don’t care enough to think.

User: A short circuit between the screen and the chair.

Hacker: The resale shop for your sensitive data after vendors and governments have finished with it.

<noun> Hacking: something that used to have its own gerund but is rebranded so it can be claimed by Silicon Valley. See dieting/body hacking, subleasing/rent hacking, etc.

Silicon Valley: To the twenty first century what Detroit was to the twentieth. 

Experienced: An employee who might recognize we have no idea what we’re doing and who will look weird in a company hoodie.

Senior: Expensive.

Machine Learning: when you can’t find a use for all the data you stole so you need to ask a computer to help.

Cloud: Nebulous and squishy agglomerations of vapor, full of darkness, turbulence, and occasional bursts of fatal lightning. Also where your data is stored.

Smartphone: A device to which the wearer’s intelligence has been outsourced.

Microsoft: See Apple (10 years ago.)

Apple: See Microsoft (10 years ago.)

Evil Empire: Whatever big tech you are talking to at the moment.

Don’t Be Evil: Do as I say, not as I do.

Ecosystem: Walled garden.

Sharing Economy: Neither shared nor an economy. Simply a class of companies that rents your possessions and labor in return for a pittance.

Content: The inconvenient text or video in the middle of all the beautiful ads. Like a lure for a fish, you don’t even get to keep it.

Filed Under: Politics, Technology Tagged With: devils dictionary, humor, slider, tech

March 22, 2019 by dratner Leave a Comment

Social Impact: A Tale of Two Stocks

In 2017 Kraft Heinz tried to acquire rival global food company Unilever for $143 billion. The deal did not complete and at least part of the reason that then-Unilever CEO Paul Polman revealed later was that it would require too many compromises in Unilever’s commitments to positive social impact.

I’m often challenged by people who believe that social impact is a nice-to-have and that big companies really can’t afford to worry about it for fear of being punished by shareholders. But while it’s anecdotal rather that a full-on trend, the story of these two stocks (UL and KHC for those of you listening at home) tells a very different story. I find it interesting since these are two huge, established, highly diversified and very sophisticated companies (each can claim to reach a broader user base that even Facebook), not a couple of hot new startups hoping to disrupt a market.

Kraft has been following the course that would be suggested by pure, traditional capitalism. Its investors introduced an approach called zero-based budgeting meant to squeeze every unnecessary penny in costs out of the system. At its core, the system calls for assuming no costs and having to justify each and every expense in each and every budgeting cycle. Theoretically, this should maximize earnings in a low-growth, somewhat commoditized industry. (It’s hard to increase the overall demand for products like ketchup, so you either need to move market share or lower costs in order to grow earnings.)

Unilever, on the other hand, has embraced social impact as a key part of its corporate strategy, both at a corporate level through its commitment to sustainable packaging and through individual brand projects like Vaseline’s Healing Project. This approach is supported by research done by leading marketing firms like Edelman and Porter Novelli that in 2018 US consumers said they valued buying a brand that agrees with their social values behind only product quality and ahead of price. This is a 20-year rising trend and is particularly noticeable in highly competitive product categories with the differentiation between two items on the shelf can be relatively small.

While this has not been the only difference in the two companies’ approaches, it has been a very significant one. And over the period since the merger fell apart, the results are stark. Between early 2017 and today, Unilever rose from around 41 to 57 while Kraft Heinz went from around 89 to 32.

It seems social impact has a strong place in capitalism after all.

Filed Under: Economics Tagged With: slider

October 4, 2018 by dratner Leave a Comment

Lie, Damn Lies, and Economic Statistics

The title of this article is a minor adjustment of a favorite saw of Mark Twain’s, but I can’t imagine he’d grudge me the use because the intent is the same as the original. Every times I read business or economic news I become more frustrated by ubiquitous (and to some degree iniquitous) use of three economic indicators. Here’s what they are, why they are wrong, and simple ways to fix them:

GDP

GDP is meant to measure the overall size of the economy and, for some reason, has now become a proxy for the overall health of the economy. If GDP is growing, things are good, right? Well, no. GDP can go up while the business sector is shrinking so long as the gap is made up for in government spending (even deficit spending.) After all, it’s just defined by this simple formula:

GDP = C + I + G + (Ex – Im)

Where C is consumer spending, I is business investment, G is government spending, and (Ex – Im) is net exports. But could it really go up while business is in decline? Sure. GDP growth in the US over the years 2012-2015 has averaged around 3.57%. Over roughly the same period, government deficit as a percentage of GDP has been 4.5% and the trade balance has remained negative. Which means that if you subtracted out the “unsustainable” or deficit part of government spending, you’d end up with a net negative GDP.

But there’s an even bigger problem. GDP can go up even while wealth and incomes consolidate to a smaller and smaller number of people. So if it’s being used to inform policy about when stimulus is needed or how taxes should raised, lowered, or structured it gives a complete false impression.

Still, GDP is a single, easily accessible number so you can see why it would be useful for sound bites and news articles, right? Maybe, but not so much since there are better numbers that do something pretty similar. Even just using median income (not average income) would be much better if you want to know how average families are actually feeling.

Unemployment

This is another frustrating one. The unemployment rate is supposed to show how easy it is for people to get jobs. Since there’s always some natural and seasonal churn in the economy it constantly needs to be adjusted and a number of around 4-5% is considered “full employment”. But this one is broken, too. It measures jobless claims as a percentage of the labor force. So it doesn’t take into account the long-term unemployed (who no longer file for benefits) or those who have given up looking since it’s just too hard.

A much better statistic to use is the labor participation rate. This is the ratio of people who are working to the overall population that’s of working age. Admittedly, it still doesn’t help identify patterns among undocumented workers, cash employment, and various other categories, but it’s a darn sight better than the unemployment rate if you only get one data point for measuring the employment situation. It would take some time to adjust since you need to know what “healthy” is, but so did unemployment rate (it isn’t obvious that 4% rather than 0% is “full” employment.)

The DOW Jones Industrial Average

The DOW is the most commonly reported stock market index and it’s also the worst. No serious analyst thinks the DOW is a good measure of the market for a few reasons.

First, it’s small. It only includes a limited number of stocks and those stocks are of the biggest companies out there. If you believe that small business can have ups and downs independent of big business (and the data would support you if you do), the DOW provides a very incomplete view.

Second, it’s price weighted. Each component of the index is weighted by price rather than by market capitalization. This is a problem since price per share is market cap divided by the number of shares and the number of shares is completely arbitrary because a company can just pick how many shares to have outstanding through splits and reverse-splits. Net net, this means a company with a high share price can have a disproportionate impact on the DOW just by having a high share price even if it’s a relatively small part of the overall economy.

For all that, using the DOW to report on the market is probably a less of a big deal than GDP or unemployment since the DOW and broader indexes like the S&P 500 move together roughly 98.5% of the time and never, to my knowledge, move in completely different directions over any significant time frame. Having said that, it’s also the easiest to fix since indexes like the S&P 500 are already widely reported and are much more accurate.

Filed Under: Economics, Politics Tagged With: dow, gdp, noslider, unemployment

September 27, 2018 by dratner Leave a Comment

Kavanaugh and the Prisoner’s Dilemma

In the discussion of the Kavanaugh hearings, there has been much attention played to fairness, justice, burden of proof, and so on, but at the end of the day a confirmation process is not a court trial. While we certainly should wish and expect for our elected representatives to take collective action in the best interests of the country (as they arguably have in the majority of past court confirmations), it’s important to remember it’s a purely political process with purely political drivers. I am not in any way making a statement about the merits of the case (which perhaps belongs on court), only suggesting a different lens that explains the senators’ behavior rather than just the facts in front of them.

In this case, the lens is the Prisoner’s Dilemma, a classic thought problem in economics. Very briefly, it explains why two people (or organizations or parties) might not work together even if it was in their common interest to do so. In its classic formulation, it explores the case of two prisoners – let’s call them D and R. Each has been arrested for a crime and they are being questioned separately. If both are silent, they will both go free since there is insufficient evidence to hold them. If D rats out R and R remains silent, D will go free since there is now D’s evidence to convict R or vice versa. If both accuse each other, they both stay in prison, but on a less charge (a 1 year sentence instead of the 3 year sentence they’d have gotten if only one of them was accused.) It’s clearly in the interest of both to remain silent so they both go free, but, absent any coordination or trust, often one of them will rat the other out in order to avoid the worst case of being accused while remaining silent. (Whether the accusation is justifiable is irrelevant for the sake of the exercise.)

While this sounds narrow, the principle has been generalized to explaining behavior as varied as international relations and sports. I think it also applies to Supreme Court picks.

I’m not suggesting that it is in the common interest of Republicans and Democrats to nominate Brett Kavanaugh, but rather that it’s in their common interest that Supreme Court appointments be fair and relatively smooth. After all, even when dealing with an opposing party’s nominee, everyone knows that at some point the shoe will be on the other foot, so why make the process toxic? Again this goes beyond the merits of the case since resistance was in full force even before the sexual assault allegations became known. I believe that the answer lies with a previous nominee, Merrick Garland.

In a fun, approachable piece on the Prisoner’s Dilemma, NPR’s Planet Money showed how if two parties play the game over and over again, different strategies yield different results. For example, if D is usually silent, R will learn that his safest path is to accuse D since they guarantee it will avoid the worst case of being accused while remaining silent. In fact, the strategy that wins in most simulations is “generous tit-for-tat” – retaliate most of the time, but every so often randomly forgive in order to avoid indefinite escalation.

How does this apply to the current Supreme Court process? Republicans denied Merrick Garland a vote for confirmation for a year despite no substantial opposition to him as a nominee (the equivalent of ratting out in the Prisoner’s Dilemma). If Democrats then allowed even unobjectionable candidates to continue to be confirmed by Republicans without a huge fight (the equivalent of being silent), they’d be transmitting to Republicans that Republicans could safely continue to block Democratic nominees without repercussion. The successful models show that it’s necessary to revenge yourself (tit-for-tat) most of the time when you are ratted out or the other side will take advantage.

But without a majority, Democrats had view options. They tried to make some stink on Neil Gorsuch’s nomination by invoking third-rail political issues but got little traction. Finding a issue of substance for attacking Brett Kavanaugh finally allowed what is, in the end, the only strategically rational behavior.

Now the question is which side will allow the process to return to normality. This is equivalent to the random forgiveness element in “generous tit-for-tat”. So long as both sides keep escalating, everyone gets badly hurt. Hopefully the next candidate to the court – Republican or Democrat – will be a reasonable choice who can be relatively easily confirmed and both sides will allow a return to normalcy. If not, the Prisoner’s Dilemma will continue to dominate and both sides will try to slash and burn each others’ candidates into oblivion. It’s just economics.

Filed Under: News, Politics

September 25, 2018 by dratner Leave a Comment

Facebook, Open the Doors

Today Attorney General Jeff Sessions will meet with states’ attorneys general to discuss his theory of bias among social media sites and what, if anything, should be done about it. Ostensibly at issue is whether social media companies like Facebook use their proprietary algorithms to suppress conservative viewpoints or boost liberal ones. It is virtually certain names like Alex Jones and Diamond and Silk will feature in the conversation.

The importance of the conversation is difficult to overstate. Although Mark Zuckerberg himself famously underestimated social media’s power to put its finger on the political scale both here and abroad (a position he has since walked back), it’s clear from both academic research, Robert Mueller’s indictments, and certainly from my own experience at the Obama campaign and beyond that in fact social media in not only key to elections, but is also aggravating political polarization in the United States.

Before even digging into the merits of Sessions’ conspiracy theory, it’s easy to call BS on Republicans who have routinely argued that corporations, in keeping with Citizens United, have first amendment rights to political speech and, therefore, spending. Facebook and Twitter are both corporations superficially much like those whose rights have been tested before. Social media companies could argue that maybe they’re biased, maybe they’re not, but they have a right to be any way we want.

But as a technologist who is neck deep in the media industry who has done my time in both the political world and Silicon Valley, I would argue that this superficial analysis should be challenged. I am not necessarily arguing that government regulation or prosecutorial discretion is the right way to handle the situation, but those are tools that may need to come into play both for the giants to be able to justify potentially unprofitable changes to their boards and investors or to get them to reexamine some of their entrenched positions.

At issue are the algorithms that determine what social media users actually see and how it gets prioritized. Feeds are no longer simply chronologies, but have become what the Internet portals of yesteryear once were – along with search, they are the jumping off point for virtually all of everyone’s online activity from news to purchases to their original purpose of keeping up with friends. They aren’t simply compilations where everything posted by people you follow is arranged chronologically interspersed with occasional ads. Instead, content is prioritized using a myriad of behavioral data (likes, shares, history of engagement with similar content, etc.) to achieve specific outcomes. The outcomes can be seen as an improved user experience (cutting through the clutter) or an optimization of the social media sites own KPIs (e.g. visits, opportunities to display ads). In Sessions’ view, they can also be seen as more subversive deliberate attempts to advance a political agenda.

Personally, I do not believe in deliberate liberal or progressive bias in these algorithms. Despite the perceived liberal leanings of a majority of people working in the tech industry, the reality is that Valley politics, especially at the billionaire executive level, continues to swing more and more libertarian. Is it possible that line engineering and technical staff have implemented some kind of tweaks and changes without the blessing of corporate overlords, a sort of Silicon Valley deep state? It’s very unlikely since the prioritization of content is absolutely key to these companies’ revenue models and is under relentless internal scrutiny.

But is it possible that, in spite of this, bias still exists? Yes. For two reasons. The first is the manipulation of these systems by outside actors. Certainly marketers around the world are obsessed with how to optimize their messages for Facebook, and so too are political operatives. Both Mueller and Cambridge Analytica whistleblower Chris Wylie have described ways that this can be done that go well beyond marketing. Since in politics many campaigns and organizations seek to discredit each other or sow discord as much as promote their own candidate, it’s actually significantly easier.

The second reason has to do with technology. We don’t know much about the specifics of the algorithms these companies use, but we can look at the foundational technologies they are doubtless based on and make some inferences. For example, the algorithms could be purely rules-based, but that’s very unlikely given especially Facebook’s lauding of the importance of behavioral targeting, the number of variables involved, and the quantity and variety of data in play. It’s more likely that it’s some combination of rules, collaborative filtering, and machine learning, the last of which is a self-proclaimed area of expertise for Facebook.

While ML is certainly a good tool for solving the problem of creating customized experiences for billions of users, it is itself subject to hidden biases. ML models are trained using large sets of real world data and the output they create is subject to the influences and selection biases of the input data. For example, you could create a machine learning model to help screen great candidates from a pool of job applications using the resumes of your past employees scored by performance as input. In theory, this should result in the model figuring out which applications are more likely to be successful at your company. But it could also result in extended hidden biases – it might continue to pick majority male candidates even if it doesn’t know the gender of the applicants based on related signals like name (e.g. “John”), sports, or even the biased makeup of other companies you’ve successfully poached from.

ML researchers have shown that understanding not just how successful algorithms are at their jobs but also understanding how they work is crucially important. For example, one team demonstrated the ability to fool the system from a prototype autonomous car into thinking a stop sign was a 45 mile per hour speed limit sign, a technique now called adversarial images. If they could manipulate the image directly, the changes weren’t even perceptible to the human eye. The point of this was that the car’s recognition system was accurate at detecting stop signs, but it wasn’t just looking for red octagons. It had learned something else that was working for it.

It is likely that if Facebook and the others are using machine learning in their prioritization algorithms they could well be subject to either hidden bias or direct manipulation. While the company would certainly do it’s best to look for these things, outside scrutiny could also be extremely helpful and would also let us know what the companies are actually optimizing for.

While the companies might claim that these algorithms represent some kind of secret sauce and that their exposure would be very damaging, that doesn’t completely stand up to scrutiny. Facebook is a monopoly or near monopoly because of network effect, not the power of its prioritization algorithm. In fact, most people do not have a positive perception of the product as represented by their net promoter score (NPS) of -21 (Twitter’s NPS of 3 is a little better but still terrible.) No competing company would emulate the algorithms with the intent of creating a customer experience like that.

It’s more likely that the exposure would be in terms of how it promotes ads, but that could even be taken out of the picture for this purpose. Just a public understanding of the prioritization of the natural news feed would be an enormous win for transparency.

The last significant pushback would be that opening the algorithms would enable manipulators like Cambridge Analytica. That is almost plausible, but relies on the dubious principle of security through obscurity. The key to securing these systems lies in trust models, identifying bad actors, and in the scrutiny of trending content. It does not lie in keeping the algorithms secret.

Social media companies have resisted classification as media companies or public utilities although they actually have a lot in common with both. Opening their algorithms for prioritizing even non-commercial content to public scrutiny could do a huge amount to restore trust, might actually improve their products, and would be one of the least invasive ways of doing it. I’m not minimizing how hard it is to do – this isn’t just code, but also large samples of anonymized user data would need to be sanitized and made available as well. But I think it’s the best way forward. If there’s nothing to hide, it would defang Jeff Sessions and the attorneys general. If there is something hidden (perhaps even from the companies themselves) it would help bring that to light.

If you want to support organizations working to make social media better, you can do it below.

Filed Under: News, Politics, Technology Tagged With: slider

September 21, 2018 by dratner Leave a Comment

The O’Hare Plan

In a year without a lot to be cheerful about in our hometown, I was excited to hear that Mayor Emanuel is putting a real push behind the idea of high speed rail service from the Chicago loop to O’Hare. This is a really good idea for a few reasons but primarily because it will increase Chicago’s global competitiveness, take a big ding out of traffic, and bring a lot of economy that has sprawled into the suburbs back downtown.

If you are running a global business today, the fact of the matter is that you’ll have a lot of employees spending a lot of time on airplanes. We simply aren’t at the point where most interactions can be done virtually. And those hours really add up. Embarrassingly, not a single US airport makes the global list of best airports and right now Chicago doesn’t make the list even in North America. One big, often cited reason for one airport to outrank another is ease of transportation to and from it. This is one of things that always holds American airports back – we haven’t got anything like the Heathrow Express. Creature comforts are great, but efficiency and predictability are way more important (which is why I’ve previously argued for a service level agreement at customs, guaranteeing 95% of passengers get through in 15 minutes or fewer.)

This matters not just for businesses headquartered in Chicago (and would certainly be a boost to companies like Amazon or GE when they consider locating here), but it also matters a lot for other key Chicago industries: tourism, hospitality, and conventions. We all know Chicago has some of the greatest restaurants, theatre, and entertainment available anywhere and we get more than 50 million tourists a year spending billions of dollars. And many of the jobs in these industries are low skill or entry level but with career paths, which is a major win in an economy increasingly focused on high tech and services.

One way to see directly what a difference better airport transportation would make is to look at Rosemont. Why does it have its own thriving hotels, conference center, offices, etc.? It doesn’t offer a better climate, better amenities, or even appreciably lower prices that downtown. It’s just way more convenient that taking a car ride that could take anywhere between 45 minutes and two hours, or trying to drag luggage (especially convention kits) on the Blue Line. A side effect of high speed service to the loop would be that a lot of that business could move back downtown.

Less discussed, but probably also top of mind for Kennedy commuters is the fact that this rail service would take some load of the highways and even off the Blue Line. That would lead to fewer delays, longer lasting roads, and less crowded el cars. It would also make it easier for O’Hare freight service, since it would need to compete with a lot fewer cars on the roads.

There has been a tendency to think of the high speed airport link as just a concession to rich businesspeople, but I believe it’s anything but that. It would be a major factor in boosting the economy and increasing the quality of transportation even for people not themselves going to the airport. It’s a worthy project and one we should pursue.

Filed Under: Politics Tagged With: slider

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