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Understanding What's Wrong With Facebook | Talking Points Memo - 0 views

talkingpointsmemo.com/...ding-whats-wrong-with-facebook evil structural tension 2nd generation internet comparative externality engine tech network manipulation architecture
shared by Javier E on 19 Sep 20 - No Cached
  • to really understand the problem with Facebook we need to understand the structural roots of that problem, how much of it is baked into the core architecture of the site and its very business model
  • much of it is inherent in the core strategies of the post-2000, second wave Internet tech companies that now dominate our information space and economy.
  • Facebook is an ingenious engine for information and ideational manipulation.
  • ...17 more annotations...
  • Good old fashioned advertising does that to a degree. But Facebook is much more powerful, adaptive and efficient.
  • Facebook is designed to do specific things. It’s an engine to understand people’s minds and then manipulate their thinking.
  • Those tools are refined for revenue making but can be used for many other purposes. That makes it ripe for misuse and bad acting.
  • The core of all second wave Internet commerce operations was finding network models where costs grow mathematically and revenues grow exponentially.
  • The network and its dominance is the product and once it takes hold the cost inputs remained constrained while the revenues grow almost without limit.
  • Facebook is best understood as a fantastically profitable nuclear energy company whose profitability is based on dumping the waste on the side of the road and accepting frequent accidents and explosions as inherent to the enterprise.
  • That’s why these companies employ so few people relative to scale and profitability.
  • That’s why there’s no phone support for Google or Facebook or Twitter. If half the people on the planet are ‘customers’ or users that’s not remotely possible.
  • The core economic model requires doing all of it on the cheap. Indeed, what Zuckerberg et al. have created with Facebook is so vast that the money required not to do it on the cheap almost defies imagination.
  • Facebook’s core model and concept requires not taking responsibility for what others do with the engine created to drive revenue.
  • It all amounts to a grand exercise in socializing the externalities and keeping all the revenues for the owners.
  • Here’s a way to think about it. Nuclear power is actually incredibly cheap. The fuel is fairly plentiful and easy to pull out of the ground. You set up a little engine and it generates energy almost without limit. What makes it ruinously expensive is managing the externalities – all the risks and dangers, the radiation, accidents, the constant production of radioactive waste.
  • managing or distinguishing between legitimate and bad-acting uses of the powerful Facebook engine is one that would require huge, huge investments of money and armies of workers to manage
  • But back to Facebook. The point is that they’ve created a hugely powerful and potentially very dangerous machine
  • The core business model is based on harvesting the profits from the commercial uses of the machine and using algorithms and very, very limited personnel (relative to scale) to try to get a handle on the most outrageous and shocking abuses which the engine makes possible.
  • Zuckerberg may be a jerk and there really is a culture of bad acting within the organization. But it’s not about him being a jerk. Replace him and his team with non-jerks and you’d still have a similar core problem.
  • To manage the potential negative externalities, to take some responsibility for all the dangerous uses the engine makes possible would require money the owners are totally unwilling and in some ways are unable to spend.
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Remembering Rush | Talking Points Memo - 0 views

talkingpointsmemo.com/...remembering-rush discourse demagogue right-wing media decline meanness bullying language
shared by Javier E on 23 Feb 21 - No Cached
  • Rush succeeded, and in part because meanness was just ramping up in conservative circles in the 80’s, and because he knew which people to stomp on.
  • The overriding traits that I observed were arrogance and meanness toward “lesser” creatures. He was big into “othering” people
  • I’ve always remembered how Rush would mock poor people, and even the towns they lived in, like Rio Linda, a small community west of Sacto. He mocked everyone who wasn’t like him
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  • So I didn’t celebrate his death as so many have, and frankly it pains me to read that stuff because it’s as cruel as he was
  • there is perhaps no other human who did more to lower the civility of public discourse, and who gave license to others to do the same. He became a truly disgusting broadcaster.
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Don't Be Surprised About Facebook and Teen Girls. That's What Facebook Is. | Talking Po... - 0 views

talkingpointsmemo.com/...n-girls-thats-what-facebook-is facebook instagram algorithm machine learning business model tobacco engagement addiction psychology brain science social media technology
shared by Javier E on 06 Oct 21 - No Cached
  • First, set aside all morality. Let’s say we have a 16 year old girl who’s been doing searches about average weights, whether boys care if a girl is overweight and maybe some diets. She’s also spent some time on a site called AmIFat.com. Now I set you this task. You’re on the other side of the Facebook screen and I want you to get her to click on as many things as possible and spend as much time clicking or reading as possible. Are you going to show her movie reviews? Funny cat videos? Homework tips? Of course, not.
  • If you’re really trying to grab her attention you’re going to show her content about really thin girls, how their thinness has gotten them the attention of boys who turn out to really love them, and more diets
  • We both know what you’d do if you were operating within the goals and structure of the experiment.
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  • This is what artificial intelligence and machine learning are. Facebook is a series of algorithms and goals aimed at maximizing engagement with Facebook. That’s why it’s worth hundreds of billions of dollars. It has a vast army of computer scientists and programmers whose job it is to make that machine more efficient.
  • the Facebook engine is designed to scope you out, take a psychographic profile of who you are and then use its data compiled from literally billions of humans to serve you content designed to maximize your engagement with Facebook.
  • Put in those terms, you barely have a chance.
  • Of course, Facebook can come in and say, this is damaging so we’re going to add some code that says don’t show this dieting/fat-shaming content but girls 18 and under. But the algorithms will find other vulnerabilities
  • So what to do? The decision of all the companies, if not all individuals, was just to lie. What else are you going to do? Say we’re closing down our multi-billion dollar company because our product shouldn’t exist?
  • why exactly are you creating a separate group of subroutines that yanks Facebook back when it does what it’s supposed to do particularly well? This, indeed, was how the internal dialog at Facebook developed, as described in the article I read. Basically, other executives said: Our business is engagement, why are we suggesting people log off for a while when they get particularly engaged?
  • what it makes me think about more is the conversations at Tobacco companies 40 or 50 years ago. At a certain point you realize: our product is bad. If used as intended it causes lung cancer, heart disease and various other ailments in a high proportion of the people who use the product. And our business model is based on the fact that the product is chemically addictive. Our product is getting people addicted to tobacco so that they no longer really have a choice over whether to buy it. And then a high proportion of them will die because we’ve succeeded.
  • . The algorithms can be taught to find and address an infinite numbers of behaviors. But really you’re asking the researchers and programmers to create an alternative set of instructions where Instagram (or Facebook, same difference) jumps in and does exactly the opposite of its core mission, which is to drive engagement
  • You can add filters and claim you’re not marketing to kids. But really you’re only ramping back the vast social harm marginally at best. That’s the product. It is what it is.
  • there is definitely an analogy inasmuch as what you’re talking about here aren’t some glitches in the Facebook system. These aren’t some weird unintended consequences that can be ironed out of the product. It’s also in most cases not bad actors within Facebook. It’s what the product is. The product is getting attention and engagement against which advertising is sold
  • How good is the machine learning? Well, trial and error with between 3 and 4 billion humans makes you pretty damn good. That’s the product. It is inherently destructive, though of course the bad outcomes aren’t distributed evenly throughout the human population.
  • The business model is to refine this engagement engine, getting more attention and engagement and selling ads against the engagement. Facebook gets that revenue and the digital roadkill created by the product gets absorbed by the society at large
  • Facebook is like a spectacularly profitable nuclear energy company which is so profitable because it doesn’t build any of the big safety domes and dumps all the radioactive waste into the local river.
  • in the various articles describing internal conversations at Facebook, the shrewder executives and researchers seem to get this. For the company if not every individual they seem to be following the tobacco companies’ lead.
  • Ed. Note: TPM Reader AS wrote in to say I was conflating Facebook and Instagram and sometimes referring to one or the other in a confusing way. This is a fair
  • I spoke of them as the same intentionally. In part I’m talking about Facebook’s corporate ownership. Both sites are owned and run by the same parent corporation and as we saw during yesterday’s outage they are deeply hardwired into each other.
  • the main reason I spoke of them in one breath is that they are fundamentally the same. AS points out that the issues with Instagram are distinct because Facebook has a much older demographic and Facebook is a predominantly visual medium. (Indeed, that’s why Facebook corporate is under such pressure to use Instagram to drive teen and young adult engagement.) But they are fundamentally the same: AI and machine learning to drive engagement. Same same. Just different permutations of the same dynamic.
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Understanding the Social Networks | Talking Points Memo - 0 views

talkingpointsmemo.com/...erstanding-the-social-networks social network extremism addiction algorithm engagement advertising psychology social media
shared by Javier E on 27 Oct 21 - No Cached
  • Even when people understand in some sense – and often even in detail – how the algorithms work they still tend to see these platforms as modern, digital versions of the town square. There have always been people saying nonsensical things, lying, unknowingly peddling inaccurate information. And our whole civic order is based on a deep skepticism about any authority’s ability to determine what’s true or accurate and what’s not. So really there’s nothing new under the sun, many people say.
  • But all of these points become moot when the networks – the virtual pubic square – are actually run by a series of computer programs designed to maximize ‘engagement’ and strong emotion for the purposes of selling advertising.
  • But really all these networks are running experiments that put us collectively into the role of Pavlov’s dogs.
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  • The algorithms are showing you things to see what you react to and showing you more of the things that prompt an emotional response, that make it harder to leave Facebook or Instagram or any of the other social networks.
  • really if your goal is to maximize engagement that is of course what you’d do since anger is a far more compelling and powerful emotion than appreciation.
  • Facebook didn’t do that. That’s coded into our neurology. Facebook really is an extremism generating machine. It’s really an inevitable part of the core engine.
  • it’s not just Facebook. Or perhaps you could say it’s not even Facebook at all. It’s the mix of machine learning and the business models of all the social networks
  • They have real upsides. They connect us with people. Show us fun videos. But they are also inherently destructive. And somehow we have to take cognizance of that – and not just as a matter of the business decisions of one company.
  • the social networks – meaning the mix of machine learning and advertising/engagement based business models – are really something new under the sun. They’re addiction and extremism generating systems. It’s what they’re designed to do.
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Wailing And Gnashing Of Teeth: Trumpers React To Draft 'Audit' Report Showing Biden Win... - 0 views

talkingpointsmemo.com/...audit-report-showing-biden-win audit arizona election confirmation bias
shared by Javier E on 25 Sep 21 - No Cached
  • the audit failed: Not only did it count Biden’s victory, but even its attempts to sow doubts about its own findings and the official results are fairly weak and rehearsed. 
  • But for Trump supporters desperate to keep the fiction going — particularly those who’ve staked their political campaigns on the Big Lie — the show needed to go on. 
  • Responding to the disappointing report, they ignored the bad news and acted as if it had affirmed their prior assumptions. And, therefore: Audits, forever and always. 
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  • “Now that the audit of Maricopa is wrapping up, we need to Audit Pima County – the 2nd largest county in AZ,” Mark Finchem, a member of the state legislature and the Trump-endorsed candidate for Arizona secretary of state tweeted. He urged readers to sign his “petition” for a Pima County audit — one that would give his campaign their personal information.
  • A state representative from Florida used the report to call for audits in every state in the country. 
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Opinion: No more union-busting. It's time for companies to give their workers what they... - 0 views

www.cnn.com/...index.html companies cnn opinion
shared by peterconnelly on 04 Jun 22 - No Cached
  • This year, workers at Amazon, Starbucks and other major corporations are winning a wave of union elections, often in the face of long odds and employer resistance. These wins are showing it's possible for determined groups of workers to break through powerful employers' use of union-busting tactics, ranging from alleged retaliatory firings to alleged surveillance and forced attendance at anti-union "captive audience meetings." But workers should not have to confront so many obstacles to exercising a guaranteed legal right to unionize and bargain for improvements in their work lives and livelihoods.
  • For decades, wage suppression, growing income inequality and persistent racial and gender wage gaps have characterized the US labor market.
  • But now, as workers are pointing the way to better workplaces and a more equitable economy, employers and policymakers need to pay attention. Policymakers must better protect workers' union rights, and employers must start respecting workers' right to participate in union elections without interference or coercion.
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  • Unions are among the most effective mechanisms available for addressing massive economic inequalities. Congress should adopt labor law reforms to better protect workers' right to organize, starting with the widely popular Protecting the Right to Organize (PRO) Act. Among other things, the PRO Act would create the first serious monetary penalties for employers that retaliate against workers for unionizing.
  • Congress must also adequately fund the National Labor Relations Board (NLRB) so the agency can enforce labor law.
  • Many US workers say they want a union, but far too few have one. Right now, workers who've won recent union elections are inviting employers to meet them as equals and start bargaining union contracts.
  • Labor unions are highly correlated with safer conditions because they give workers a voice in setting workplace policies and the ability to engage management in addressing concerns without fear of retaliation.
  • The Black-led, multiracial committees that have led organizing drives at Amazon warehouses and the young women baristas leading breakthrough organizing victories at Starbucks are changing the public face of the labor movement in powerful and promising ways.
  • Two-thirds of union workers are women and/or workers of color.
  • In any company, the transparency and consistency of a union contract that sets wage rates, scheduled raises and procedures for promotions helps guard against forms of discriminatory bias that otherwise disadvantage women and workers of color.
  • Unionizing workers will continue to need extraordinary solidarity, persistence and public support in order to succeed. This is a moment of opportunity for all of us. Anyone ready to start reversing the worst economic inequalities the US has seen in almost a century can choose now to join and support workers who are organizing unions.
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CNN boss' message for staffers: Cool it with 'Breaking News' banner - 0 views

www.cnbc.com/...-the-breaking-news-banner.html cnn breaking news
shared by peterconnelly on 04 Jun 22 - No Cached
  • New CNN chief Chris Licht has a message for his employees: not everything needs to be labeled “Breaking News.”
  • Licht came to the conclusion there should be parameters around when to use the red chyron
  • “This is a great starting point to try to make ‘Breaking News’ mean something BIG is happening,” Licht wrote in the memo, which CNBC has obtained. “We are truth-tellers, focused on informing, not alarming our viewers. You’ve already seen far less of the ‘Breaking News’ banner across our programming. The tenor of our voice holistically has to reflect that.”
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  • “I would like to see CNN evolve back to the kind of journalism that it started with,” Malone told CNBC in November.
  • Zaslav said in April that CNN’s measured take on news is essential for “a civilized society” and crucial for it to avoid the image of being an “advocacy” network.
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His Job Was to Make Instagram Safe for Teens. His 14-Year-Old Showed Him What the App W... - 0 views

www.wsj.com/...ens-harassment-safety-5d991be1 social media bias technology psychology meta facebook instagram
shared by Javier E on 05 Nov 23 - No Cached
  • The experience of young users on Meta’s Instagram—where Bejar had spent the previous two years working as a consultant—was especially acute. In a subsequent email to Instagram head Adam Mosseri, one statistic stood out: One in eight users under the age of 16 said they had experienced unwanted sexual advances on the platform over the previous seven days.
  • For Bejar, that finding was hardly a surprise. His daughter and her friends had been receiving unsolicited penis pictures and other forms of harassment on the platform since the age of 14, he wrote, and Meta’s systems generally ignored their reports—or responded by saying that the harassment didn’t violate platform rules.
  • “I asked her why boys keep doing that,” Bejar wrote to Zuckerberg and his top lieutenants. “She said if the only thing that happens is they get blocked, why wouldn’t they?”
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  • For the well-being of its users, Bejar argued, Meta needed to change course, focusing less on a flawed system of rules-based policing and more on addressing such bad experiences
  • The company would need to collect data on what upset users and then work to combat the source of it, nudging those who made others uncomfortable to improve their behavior and isolating communities of users who deliberately sought to harm others.
  • “I am appealing to you because I believe that working this way will require a culture shift,” Bejar wrote to Zuckerberg—the company would have to acknowledge that its existing approach to governing Facebook and Instagram wasn’t working.
  • During and after Bejar’s time as a consultant, Meta spokesman Andy Stone said, the company has rolled out several product features meant to address some of the Well-Being Team’s findings. Those features include warnings to users before they post comments that Meta’s automated systems flag as potentially offensive, and reminders to be kind when sending direct messages to users like content creators who receive a large volume of messages. 
  • Meta’s classifiers were reliable enough to remove only a low single-digit percentage of hate speech with any degree of precision.
  • Bejar was floored—all the more so when he learned that virtually all of his daughter’s friends had been subjected to similar harassment. “DTF?” a user they’d never met would ask, using shorthand for a vulgar proposition. Instagram acted so rarely on reports of such behavior that the girls no longer bothered reporting them. 
  • Meta’s own statistics suggested that big problems didn’t exist. 
  • Meta had come to approach governing user behavior as an overwhelmingly automated process. Engineers would compile data sets of unacceptable content—things like terrorism, pornography, bullying or “excessive gore”—and then train machine-learning models to screen future content for similar material.
  • While users could still flag things that upset them, Meta shifted resources away from reviewing them. To discourage users from filing reports, internal documents from 2019 show, Meta added steps to the reporting process. Meta said the changes were meant to discourage frivolous reports and educate users about platform rules. 
  • The outperformance of Meta’s automated enforcement relied on what Bejar considered two sleights of hand. The systems didn’t catch anywhere near the majority of banned content—only the majority of what the company ultimately removed
  • “Please don’t talk about my underage tits,” Bejar’s daughter shot back before reporting his comment to Instagram. A few days later, the platform got back to her: The insult didn’t violate its community guidelines.
  • Also buttressing Meta’s statistics were rules written narrowly enough to ban only unambiguously vile material. Meta’s rules didn’t clearly prohibit adults from flooding the comments section on a teenager’s posts with kiss emojis or posting pictures of kids in their underwear, inviting their followers to “see more” in a private Facebook Messenger group. 
  • “Mark personally values freedom of expression first and foremost and would say this is a feature and not a bug,” Rosen responded
  • Narrow rules and unreliable automated enforcement systems left a lot of room for bad behavior—but they made the company’s child-safety statistics look pretty good according to Meta’s metric of choice: prevalence.
  • Defined as the percentage of content viewed worldwide that explicitly violates a Meta rule, prevalence was the company’s preferred measuring stick for the problems users experienced.
  • According to prevalence, child exploitation was so rare on the platform that it couldn’t be reliably estimated, less than 0.05%, the threshold for functional measurement. Content deemed to encourage self-harm, such as eating disorders, was just as minimal, and rule violations for bullying and harassment occurred in just eight of 10,000 views. 
  • “There’s a grading-your-own-homework problem,”
  • Meta defines what constitutes harmful content, so it shapes the discussion of how successful it is at dealing with it.”
  • It could reconsider its AI-generated “beauty filters,” which internal research suggested made both the people who used them and those who viewed the images more self-critical
  • the team built a new questionnaire called BEEF, short for “Bad Emotional Experience Feedback.
  • A recurring survey of issues 238,000 users had experienced over the past seven days, the effort identified problems with prevalence from the start: Users were 100 times more likely to tell Instagram they’d witnessed bullying in the last week than Meta’s bullying-prevalence statistics indicated they should.
  • “People feel like they’re having a bad experience or they don’t,” one presentation on BEEF noted. “Their perception isn’t constrained by policy.
  • they seemed particularly common among teens on Instagram.
  • Among users under the age of 16, 26% recalled having a bad experience in the last week due to witnessing hostility against someone based on their race, religion or identity
  • More than a fifth felt worse about themselves after viewing others’ posts, and 13% had experienced unwanted sexual advances in the past seven days. 
  • The vast gap between the low prevalence of content deemed problematic in the company’s own statistics and what users told the company they experienced suggested that Meta’s definitions were off, Bejar argued
  • To minimize content that teenagers told researchers made them feel bad about themselves, Instagram could cap how much beauty- and fashion-influencer content users saw.
  • Proving to Meta’s leadership that the company’s prevalence metrics were missing the point was going to require data the company didn’t have. So Bejar and a group of staffers from the Well-Being Team started collecting it
  • And it could build ways for users to report unwanted contacts, the first step to figuring out how to discourage them.
  • One experiment run in response to BEEF data showed that when users were notified that their comment or post had upset people who saw it, they often deleted it of their own accord. “Even if you don’t mandate behaviors,” said Krieger, “you can at least send signals about what behaviors aren’t welcome.”
  • But among the ranks of Meta’s senior middle management, Bejar and Krieger said, BEEF hit a wall. Managers who had made their careers on incrementally improving prevalence statistics weren’t receptive to the suggestion that the approach wasn’t working. 
  • After three decades in Silicon Valley, he understood that members of the company’s C-Suite might not appreciate a damning appraisal of the safety risks young users faced from its product—especially one citing the company’s own data. 
  • “This was the email that my entire career in tech trained me not to send,” he says. “But a part of me was still hoping they just didn’t know.”
  • “Policy enforcement is analogous to the police,” he wrote in the email Oct. 5, 2021—arguing that it’s essential to respond to crime, but that it’s not what makes a community safe. Meta had an opportunity to do right by its users and take on a problem that Bejar believed was almost certainly industrywide.
  • fter Haugen’s airing of internal research, Meta had cracked down on the distribution of anything that would, if leaked, cause further reputational damage. With executives privately asserting that the company’s research division harbored a fifth column of detractors, Meta was formalizing a raft of new rules for employees’ internal communication.
  • Among the mandates for achieving “Narrative Excellence,” as the company called it, was to keep research data tight and never assert a moral or legal duty to fix a problem.
  • “I had to write about it as a hypothetical,” Bejar said. Rather than acknowledging that Instagram’s survey data showed that teens regularly faced unwanted sexual advances, the memo merely suggested how Instagram might help teens if they faced such a problem.
  • The hope that the team’s work would continue didn’t last. The company stopped conducting the specific survey behind BEEF, then laid off most everyone who’d worked on it as part of what Zuckerberg called Meta’s “year of efficiency.
  • If Meta was to change, Bejar told the Journal, the effort would have to come from the outside. He began consulting with a coalition of state attorneys general who filed suit against the company late last month, alleging that the company had built its products to maximize engagement at the expense of young users’ physical and mental health. Bejar also got in touch with members of Congress about where he believes the company’s user-safety efforts fell short. 
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