recentpopularlog in

robertogreco : trustworthiness   4

Media Literacy Is About Where To Spend Your Trust. But You Have To Spend It Somewhere. | Hapgood
"A lot of approaches to online media literacy highlight “debunking” and present a large a portion of cases where students debunk tree octopuses and verifiably false things. And show students how they are manipulated, etc.

And this is good in the right amounts. There’s a place for it. It should comprise much of your curriculum.

But the core of media literacy for me is this question of “where you spend your trust.” And everything has to be evaluated in that framework.

There’s not an option to not trust anyone, at least not an option that is socially viable. And societies without trust come to bad ends. Students are various, of course, but what I find with many students is they are trust misers — they don’t want to spend their trust anywhere, and they think many things are equally untrustworthy. And somehow they have been trained to think this makes them smarter than the average bear.

A couple stories will illustrate the problem. I was once working with a bunch of students and comparing Natural News (a health supplements site which specializes in junk science claims) and the Mayo Clinic, one of the most respected outfits out there. OK, I say, so what’s the problem with taking advice from Natural News?

Well, says a student, they make their money selling supplements, and so they have an incentive to talk down traditional medicine.

I beam like a proud papa. Good analysis!

“And,” the student continues, “the Mayo Clinic is the same way. They make money off of patients so they want to portray regular hospitals as working.”

Houston, we have a problem.

I was in an upper division class another time and we were looking at an expert in a newspaper cited for his background in the ethnobiology of issues around the study of birds. I did what I encourage students to do in such cases: as a sanity check, make sure that the person being quoted as an academic expert has a publication record in the relevant area, preferably with a cite or two. (There are other varieties of expertise, of course, but in this case the claimed expertise was academic).

The record comes up. This guy’s top article on birds, biologists, and indigenous knowledge has something like 34 citations in Google Scholar. “So what do you think?” I ask them.

“Eh,” they say. “Not great.”

This was, mind you, not a room full of published ethnobiologists. And the ethnobiologist quoted in the article was not claiming to overturn the fundamental insights of ethnobiology, or anything requiring extraordinary evidence.

So 34 other experts had considered this person’s niche work worth talking about but hey, we’re still not sure this guy’s worth listening to on a subject we know nothing about and in which he is making rather moderate claims…

Hrmm.

Another class, looking at Canadian paper the National Post, noted that while it was a “real” paper with a real staff, the Wikipedia page on it noted a controversy about some wrong information they published in 2006, where the editor had to actually pen an apology. “So kind of half-and-half, right?”

I’ve referred to this before as trust compression, the tendency for students to view vastly different levels of credibility of sources all as moderately or severely compromised. Breitbart is funded by the Mercers, who are using it directly to influence political debate, but the Washington Post is also owned by Jeff Bezos who donated to Democrats. So it’s a wash. And yes, we have the word of an expert in a subject where she has multiple cites against the word of a lobbying group but neither one is perfect really. Everyone’s got an agenda, nobody knows everything, and there’s not 100% agreement on anything anyway.

You see this in areas outside of expertise as well, incidentally. With quotes I often ask students (and faculty!) to source the quote and then say if the quote was taken out of context. The answer? You’ll always get a range from “completely taken out of context” to “somewhat taken out of context”. That upper register of “Nope, that quote was used correctly” is something you really have to coax the students into.

I don’t quite know how to square this with the gullibility often on display, except to say that very often that gullibility is about not being able (or willing) to distinguish gradations of credibility.

This should scare you, and it has to be at the core of what we teach — to teach students they need to decompress their trust, get out of that mushy middle, and make real distinctions. And ultimately, put their trust somewhere. Otherwise we end up with what Hannah Arendt so accurately described as the breeding ground of totalitarianism:
In an ever-changing, incomprehensible world the masses had reached the point where they would, at the same time, believe everything and nothing, that everything was possible and that nothing was true… Mass Propaganda discovered that its audience was ready at all times to believe the worst, no matter how absurd, and did not particularly object to being deceived because it held every statement to be a lie anyhow…

I do believe this insight — that trust has to be spent somewhere and that our problem is not gullibility, but rather the gullibility of cynics — has to be at the core of what we teach and how we teach it. You have some trust, and you have to be willing to spend it somewhere. So enough of the “this isn’t great either”, enough of the “eh”. What’s your best option for spending that trust? Why?

If everything is compromised, then everything can be ignored, and filtering is simply a matter of choosing what you want to hear. And students will economize that lesson in a heartbeat. In fact, I’m worried they already have, and it’s up to us to change that."
medialiteracy  mikecaulfield  internet  web  media  authority  trust  hannaharendt  trustworthiness  online  journalism  bias  expertise  gullibility  propaganda  2018 
february 2018 by robertogreco
Physiognomy’s New Clothes – Blaise Aguera y Arcas – Medium
"In 1844, a laborer from a small town in southern Italy was put on trial for stealing “five ricottas, a hard cheese, two loaves of bread […] and two kid goats”. The laborer, Giuseppe Villella, was reportedly convicted of being a brigante (bandit), at a time when brigandage — banditry and state insurrection — was seen as endemic. Villella died in prison in Pavia, northern Italy, in 1864.

Villella’s death led to the birth of modern criminology. Nearby lived a scientist and surgeon named Cesare Lombroso, who believed that brigantes were a primitive type of people, prone to crime. Examining Villella’s remains, Lombroso found “evidence” confirming his belief: a depression on the occiput of the skull reminiscent of the skulls of “savages and apes”.

Using precise measurements, Lombroso recorded further physical traits he found indicative of derangement, including an “asymmetric face”. Criminals, Lombroso wrote, were “born criminals”. He held that criminality is inherited, and carries with it inherited physical characteristics that can be measured with instruments like calipers and craniographs [1]. This belief conveniently justified his a priori assumption that southern Italians were racially inferior to northern Italians.

The practice of using people’s outer appearance to infer inner character is called physiognomy. While today it is understood to be pseudoscience, the folk belief that there are inferior “types” of people, identifiable by their facial features and body measurements, has at various times been codified into country-wide law, providing a basis to acquire land, block immigration, justify slavery, and permit genocide. When put into practice, the pseudoscience of physiognomy becomes the pseudoscience of scientific racism.

Rapid developments in artificial intelligence and machine learning have enabled scientific racism to enter a new era, in which machine-learned models embed biases present in the human behavior used for model development. Whether intentional or not, this “laundering” of human prejudice through computer algorithms can make those biases appear to be justified objectively.

A recent case in point is Xiaolin Wu and Xi Zhang’s paper, “Automated Inference on Criminality Using Face Images”, submitted to arXiv (a popular online repository for physics and machine learning researchers) in November 2016. Wu and Zhang’s claim is that machine learning techniques can predict the likelihood that a person is a convicted criminal with nearly 90% accuracy using nothing but a driver’s license-style face photo. Although the paper was not peer-reviewed, its provocative findings generated a range of press coverage. [2]
Many of us in the research community found Wu and Zhang’s analysis deeply problematic, both ethically and scientifically. In one sense, it’s nothing new. However, the use of modern machine learning (which is both powerful and, to many, mysterious) can lend these old claims new credibility.

In an era of pervasive cameras and big data, machine-learned physiognomy can also be applied at unprecedented scale. Given society’s increasing reliance on machine learning for the automation of routine cognitive tasks, it is urgent that developers, critics, and users of artificial intelligence understand both the limits of the technology and the history of physiognomy, a set of practices and beliefs now being dressed in modern clothes. Hence, we are writing both in depth and for a wide audience: not only for researchers, engineers, journalists, and policymakers, but for anyone concerned about making sure AI technologies are a force for good.

We will begin by reviewing how the underlying machine learning technology works, then turn to a discussion of how machine learning can perpetuate human biases."



"Research shows that the photographer’s preconceptions and the context in which the photo is taken are as important as the faces themselves; different images of the same person can lead to widely different impressions. It is relatively easy to find a pair of images of two individuals matched with respect to age, race, and gender, such that one of them looks more trustworthy or more attractive, while in a different pair of images of the same people the other looks more trustworthy or more attractive."



"On a scientific level, machine learning can give us an unprecedented window into nature and human behavior, allowing us to introspect and systematically analyze patterns that used to be in the domain of intuition or folk wisdom. Seen through this lens, Wu and Zhang’s result is consistent with and extends a body of research that reveals some uncomfortable truths about how we tend to judge people.

On a practical level, machine learning technologies will increasingly become a part of all of our lives, and like many powerful tools they can and often will be used for good — including to make judgments based on data faster and fairer.

Machine learning can also be misused, often unintentionally. Such misuse tends to arise from an overly narrow focus on the technical problem, hence:

• Lack of insight into sources of bias in the training data;
• Lack of a careful review of existing research in the area, especially outside the field of machine learning;
• Not considering the various causal relationships that can produce a measured correlation;
• Not thinking through how the machine learning system might actually be used, and what societal effects that might have in practice.

Wu and Zhang’s paper illustrates all of the above traps. This is especially unfortunate given that the correlation they measure — assuming that it remains significant under more rigorous treatment — may actually be an important addition to the already significant body of research revealing pervasive bias in criminal judgment. Deep learning based on superficial features is decidedly not a tool that should be deployed to “accelerate” criminal justice; attempts to do so, like Faception’s, will instead perpetuate injustice."
blaiseaguerayarcas  physiognomy  2017  facerecognition  ai  artificialintelligence  machinelearning  racism  bias  xiaolinwu  xi  zhang  race  profiling  racialprofiling  giuseppevillella  cesarelombroso  pseudoscience  photography  chrononet  deeplearning  alexkrizhevsky  ilyasutskever  geoffreyhinton  gillevi  talhassner  alexnet  mugshots  objectivity  giambattistadellaporta  francisgalton  samuelnorton  josiahnott  georgegiddon  charlesdarwin  johnhoward  thomasclarkson  williamshakespeare  iscnewton  ernsthaeckel  scientificracism  jamesweidmann  faception  criminality  lawenforcement  faces  doothelange  mikeburton  trust  trustworthiness  stephenjaygould  philippafawcett  roberthughes  testosterone  gender  criminalclass  aggression  risk  riskassessment  judgement  brianholtz  shermanalexie  feedbackloops  identity  disability  ableism  disabilities 
may 2017 by robertogreco
Trust Me - Freakonomics Freakonomics
"Societies where people trust one another are healthier and wealthier. In the U.S. (and the U.K. and elsewhere), social trust has been falling for decades — in part because our populations are more diverse. What can we do to fix it?"



"HALPERN: We almost seem to hardly notice that it’s there. So it’s incredibly consequential and we see it in lots of areas of policy that we touch on.

DUBNER: So you write this about low trust: “Low trust implies a society where you have to keep an eye over your shoulder, where deals need lawyers instead of handshakes, where you don’t see the point of paying your tax or recycling your rubbish since you doubt your neighbor will do so, and where employ your cousin or your brother-in-law to work for you rather than a stranger who’d probably be much better at the job.” So that has all kinds of business and ultimately economic implications. However, when you talk about high trust being good for us on a personal level, whether it’s health or individual income, do the two necessarily go in hand? In other words, can we have a society that has a business climate where there isn’t a lot of trust and, therefore, you do need all those lawyers instead of the handshakes, but where you have good social trust among neighbors, family and friends, communities and so on, or are they really the same thing that you’re talking about?

HALPERN: Well, there is a key distinction and Bob Putnam has often made this too, between what’s sometimes called bonding social capital and bridging social capital.

PUTNAM: Social capital is about social networks. But not all social networks are identical, and one important distinction is between ties that link us to other people like us, that’s called bonding social capital.

HALPERN: Bonding social capital often refers to your closeness to your friends, your relatives, those that are immediately around you. It’s particularly important, it turns out for, things such as health outcomes.

PUTNAM: Because, empirically, if you get sick, the people who are likely to bring you chicken soup are likely to represent your bonding social capital."



"PUTNAM: What strategies I would want to emphasize for moving in a positive direction would be more contexts in which people connect with one another across lines of race or economics or gender or age."



"HALPERN: People that go to university end up trusting much more than those who don’t, particularly when they go away residentially. It doesn’t look like it’s explained by income alone. So there’s something about the experience of going off as a young person in an environment where you have lots of other young people from different backgrounds and so on, hopefully, and different ethnicities. You learn the habits of trust because you’re in an environment where you can trust other people; they are trustworthy. And you internalize these habits and you take them with you the rest of your life. So we tend to not think of going away to university as being the reason why you’re doing it is to build social capital and social trust, we think about learning skills and so on, but it may well be that it has as much, or even more value, in terms of culturing social trust going forward. The question is: do you have to do that in university, can you do it another way? So in the U.K., following partly an American lead, the government has championed a national citizen service. And what this means is for every young person, essentially a 17-year-old, increasingly, starts off with a — not everyone does it alone, but more and more every single year, goes and does voluntary experience, community service. This deliberately includes a couple of weeks which are residential and deliberately includes mixing with people from all different walks of life. Look, it’s only limited data, but in terms of before-and-after data, we see significant impacts in terms of higher levels of trust between groups and individuals, as well as instantly higher levels of life satisfaction and well-being too. So it looks like we can do something about it."



"HALPERN: In the most recent data, it looks like it’s one of the biggest risers. So the Netherlands had pretty similar levels of social trust in the 1980s to America and the U.K., but whereas we have now drifted down towards sort of 30-odd percent, they are now up close to 70 percent in levels of those who think others can be trusted.

DUBNER: What would you say it’s caused by?

HALPERN: Well, I mean, one of the characteristics of the Netherlands, and you have to be a bit careful when you pick off one country, is it has wrestled quite hard with the issues of, not just inequality, but social differences. They’ve really tried to do a lot in relation to making people essentially build cohesion. Particularly Amsterdam, is a very famous area for — it’s long been an extremely multicultural city. It’s had issues over that over time, but they’ve really in a sort of succession of governments have tried to quite actively make groups get along with each other in quite an active way. So that may itself, of course, root in the Netherlands, it’s quite a deep culture of a strong sense of the law, being trustworthy and that contracts will be honored and so on. It’s what helped to power its economic success in previous centuries, so it does have that tradition also to draw on."



"PUTNAM: I looked hard to find explanations and television, I argued, is really bad for social connectivity for many reasons.

“More television watching,” Putnam wrote, “means less of virtually every form of civic participation and social involvement.”

HALPERN: As Bob sometimes put it, I think, rather elegantly, when we were looking forward in terms of technology or the Internet and of course, even pre-Facebook and so on, would it be, in his words, a “fancy television”? In other words, it will isolate us more and more. Or would it be a “fancy telephone” and would connect us more and more? Because technology has both those capabilities. So when I played video games when I was a kid, you basically did them mostly by yourself or with a friend. When I look at my teenage kids playing videos, they’re actually talking to each other all the time. To some extent it looks like, to me, that we get the technology that we want, and even this is true at sort of a societal level. So one of the arguments you can make, in my view is true anyway, by explaining some of these differences in the trajectories across countries is in Anglo-Saxon countries, we’ve often used our wealth to buy technology and other experiences. That means we don’t have to deal with other people — the inconveniences of having to go to a concert where I have to listen to music I really like, I can just stay at home and just watch what I want and so on and choose it. And even in the level of, if I think about my kids versus me growing up, I mean when I was growing up we had one TV and there were five kids in the household. You know, had to really negotiate pretty hard about what we were going to watch. My kids don’t have to do that and probably not yours either. There are more screens in the house than there are people. They can all go off and do their own thing. To some extent, that is us using our wealth to escape from having to negotiate with other people, but that isn’t necessarily the case. Some people and some countries seem to use their wealth more to find ways of connecting more with other people. And the technology has both these capabilities and we can’t just blame it. It’s the choices we’re making and how we use it and the technology which we’re, kind of, asking and bringing forth.

DUBNER: It reminds me a bit of — we once looked into the global decline of hitchhiking, for instance. One of the central reasons being that people no longer trusted strangers to not kill each other, really, is what it boiled down to, even though there was apparently very little killing involved, but just the fear of one. And yet now, Uber is a 60-some billion-dollar company that’s basically all about using technology to lure a complete stranger into your car. Which, I guess, argues, if nothing else, the fact that technology can be harnessed very much in either direction.

HALPERN: That’s right. Indeed, so, as you say, there’s actually two points here, and there’s a really important behavioral one, which I think we’ve only figured out in recent years to bring together these different literatures, how does it relate to behavioral scientists versus those people studying social capital? We look like we have certain systematic biases about how we estimate whether we think other people can be trusted. And in essence, we overestimate quite systematically the prevalence of bad behavior. We overestimate the number of people who are cheating on their taxes or take a sickie off work or do other kinds of bad things. This doesn’t seem to be just the media, although that may reinforce it. It seems to be a bit how we’re wired as human beings. So why is that relevant and why does this have to do with technology? Actually, technology can help you solve some of those issues. So when you’re buying something on eBay or you’re trying to decide where to go using, you know Trip Advisor, you’re actually getting some much better information from the experiences of other people as opposed to your guesstimate, which is often systematically biased. So it turns out it’s a way we can sometimes use technology to solve some of these trust issues. Not just in relation to specific products and “Should I buy this thing from this person?” but, potentially, more generally in relation to how do we trust other people because, ultimately, this social trust question must rest on something. It must be a measure of actual trustworthiness. "
trust  diversity  socialtrust  2016  us  society  socialunity  via:davidtedu  trustworthiness  socialcapital  australia  uk  netherlands  davidhalpern  stephendubner  bobputnam  italy  corruption  socialnetworks  civics  government  governance  community  brazil  brasil  norway  edglaeser  tobymoscowitz  hunterwendelstedt  ethnicity  stockholm  education  colleges  universities  military  athletics  multiculturalism  culture  law  economics  behavior  technology  videogames  socialmedia  television  tv  toolsforconviviality  hitchhiking 
november 2016 by robertogreco
more than 95 theses — Now how about this: We know that greenhouse gases...
"
Now how about this: We know that greenhouse gases are producing destabilizing changes in the Earth’s climate. And that human beings evolved from other species over millions of years. And that Barack Obama is a Christian. And that Hillary Clinton had nothing to do with the death of Vince Foster.

Large numbers of Americans deny those and many other assertions. Why? Because the trustworthiness of the authorities that make the claims has been under direct and continuous attack for the past several decades — and because the internet has given a voice to every kook who makes a contrary assertion. What we’re left with is a chaos of competing claims, none of which has the authority to dispel the others as untrue.


—Damon Linker [https://theweek.com/articles/645664/rise-american-conspiracy-theory ]

Most of what Damon says here is exactly right, but he’s leaving out another major factor: the toxic combination of habitual arrogance and habitual error that afflicts so many of our “authorities.” Consider the amazingly inaccurate track record of expert economic forecasters [http://www.scientificamerican.com/article/finance-why-economic-models-are-always-wrong/ ]. Consider the vast claims made by neuroscientists wielding fMRI machines — machines that consistently yield false results [http://arstechnica.com/science/2016/07/algorithms-used-to-study-brain-activity-may-be-exaggerating-results/ ]. And consider the constant cheerleading for expert bullshit from much of the media.

It is true that “the trustworthiness of the authorities that make the claims has been under direct and continuous attack for the past several decades” — but it is also true that some of those authorities deserve to be attacked, and indeed to be attacked more strongly than they are. So in this situation, what is the ordinary person to do? How is she supposed to tell the difference between the reliable expertise of climate scientists and the unreliable “expertise” of yet another neuroscience charlatan? Isn’t it perfectly understandable that in such a noisy environment she will say, “Yeah, right, ‘experts’ — who needs that crap?”"
alanjacobs  damonlinker  arrogance  experts  trustworthiness  science  neuroscience  2016  confidence  skepticism  economics  economists  politics  debate  information  criticalthinking  media 
september 2016 by robertogreco

Copy this bookmark:





to read