On "The Media"

On “The Media”

“The media” is a  term that is frequently used to categorize journalists, newspapers, and broadcast news shows. We often use “the media” in a negative way, complaining about coverage of events in unfair and oversimplified ways. “The media” always seems to have an agenda, a narrative, and a specific concern plucked from the zeitgeist that will fade away without a real resolution. But this idea is a bit misleading. Categorizing only news sources as “the media” misses out on a lot of media consumption that we engage with every day. It also lumps together news organizations and sources that have vastly different ways of operating, different profit motives, and different general beliefs. Even within a single news or media source there can be things that are terrible, things that are marvelous, and things that we barely notice.
Challenges with “the media” have existed as long as news and media have existed. Books, even fiction books, have been burned and banned almost as long as books have existed. People expressing heretical views against churches or governments have also received the same fate across human history.  But “the media” has been a lens through which we have understood the world past and present. Expanding our view of media to include books, movies, podcasts, and even TikTok videos shows us how media consumption can be cultural cornerstones of our highest values and simultaneously cesspools of rot.
In the George Herriman biography Krazy, author Michael Tisserand includes a quote from a critique written by Gilbert Seldes in the Pittsburgh Sun in the 1920’s. Tisserand’s passage reads:
“In his initial appraisal of Krazy Kat [George Herriman’s celebrated comic strip], he wrote that the cult of the genius of the comic strip who has created the fantastic little monster is a growing one. He added if we have to condemn utterly the press which demoralizes all thought and makes ugly all things capable of beauty, we must still be gentle with it, because Krazy Kat, the invincible and joyous, is a creature of the press, inconceivable without its foundation of cheapness and stupidity. He is there to enliven and encourage and to give much delight.
I really like this quote when viewed through the lens of “the media” that I have been trying to lay out in this post, even though Seldes uses “the press” in the quote above. Categorizing “the media” as entirely worthless or negative or alternatively categorizing “the media” as a cornerstone of democracy is an overly broad brush with which to paint news and information ecosystems. There are things we may hate about “the media” but there are also things we may find invaluable and necessary. Thinking clearly about the media requires that we delve into the particulars, understand the profit motives, understand the competition, and understand the forces that drive the things we like and dislike.
Individually, we are probably powerless to change the course of “the media” or how we talk about “the media.” However, we can think about the choices we make in relation to “the media” and to our friends, family, and colleagues. We can engage in meaningful and deep topics, or we can become enraged over shallow and meaningless topics. We can enjoy the cultural reflections of the shallow or we can criticize them. Ultimately, “the media” is a product of our humanity, and we can project onto it what we want, but we shouldn’t categorize an entire institution as rotten or democracy saving as a whole. “The Media” is complex and has multiple layers running throughout each interconnected element.
Data Mining is a First Step

Data Mining is a First Step

From big tech companies, sci-fi movies, and policy entrepreneurs data mining is presented as a solution to many of our problems. With traffic apps collecting mountains of movement data, governments collecting vast amounts of tax data, and heath-tech companies collecting data for every step we take, the promise of data mining is that our sci-fi fantasies will be realized here on earth in the coming years. However, data mining is only a first step on a long road to the development of real knowledge that will make our world a better place. The data alone is interesting and our computing power to work with big data is astounding, but data mining can’t give us answers, only interesting correlations and statistics.
In The Book of Why Judea Pearl writes:
“It’s easy to understand why some people would see data mining as the finish rather than the first step. It promises a solution using available technology. It saves us, as well as future machines, the work of having to consider and articulate substantive assumptions about how the world operates. In some fields our knowledge may be in such an embryonic state that we have no clue how to begin drawing a model of the world. But big data will not solve this problem. The most important part of the answer must come from such a model, whether sketched by us or hypothesized and fine-tuned by machines.”
Big data can give us insights and help us identify unexpected correlations and associations, but identifying unexpected correlations and associations doesn’t actually tell us what is causing the observations we make. The messaging of massive data mining is that we will suddenly understand the world and make it a better place. The reality is that we have to develop hypotheses about how the world works based on causal understandings of the interactions between various factors of reality. This is crucial or we won’t be able to take meaningful action based what comes from our data mining. Without developing causal hypotheses we cannot experiment with associations and continue to learn, we can only observe what correlations come from big data. Using the vast amounts of data we are collecting is important, but we have to have a goal to work toward and a causal hypothesis of how we can reach that goal in order for data mining to be meaningful.
Stories from Bid Data

Stories from Big Data

Dictionary.com describes datum (the singular of data) as “a single piece of information; any fact assumed to be a matter of direct observation.” So when we think about big data, we are thinking about massive amounts of individual pieces of information or individual facts from direct observation. Data simply are what they are, facts and individual observations in isolation.
On the other hand Dictionary.com defines information as “knowledge communicated or received concerning a particular fact or circumstance.” Information is the knowledge, story, and ideas we have about the data. These two definitions are important for thinking about big data. We never talk about big information, but the reality is that big data is less important than the knowledge we generate from the data, and that isn’t as objective as the individual datum.
In The Book of Why Judea Pearl writes, “a generation ago, a marine biologist might have spent months doing a census of his or her favorite species. Now the same biologist has immediate access online to millions of data points on fish, eggs, stomach contents, or anything else he or she wants. Instead of just doing a census, the biologist can tell a story.” Science has become contentious and polarizing recently, and part of the reason has to do with the stories that we are generating based on the big data we are collecting. We can see new patterns, new associations, new correlations, and new trends in data from across the globe. As we have collected this information, our impact on the planet, our understanding of reality, and how we think about ourselves in the universe has changed. Science is not simply facts, that is to say it is not just data. Science is information, it is knowledge and stories that have continued to challenge the narratives we have held onto as a species for thousands of years.
Judea Pearl thinks it is important to recognize the story aspect of big data. He thinks it is crucial that we understand the difference between data and information, because without doing so we turn to the data blindly and can generate an inaccurate story based on what we see. He writes,
“In certain circles there is an almost religious faith that we can find the answers to … questions in the data itself, if only we are sufficiently clever at data mining. However, readers of this book will know that this hype is likely to be misguided. The questions I have just asked are all causal, and causal questions can never be answered from data alone.”
Big data presents us with huge numbers of observations and facts, but those facts alone don’t represent causal structures or deeper interactions within reality. We have to generate information from the data and combine that new knowledge with existing knowledge and causal hypothesis to truly learn something new from big data. If we don’t then we will simply be identifying meaningless correlations without truly understanding what they mean or imply.
Regression Coefficients

Regression Coefficients

Statistical regression is a great thing. We can generate a scatter plot, generate a line of best fit, and measure how well that line describes the relationship between the individual points within the data. The better the line fits (the more that individual points stick close to the line) the better the line describes the relationships and trends in our data. However, this doesn’t mean that the regression coefficients tell us anything about causality. It is tempting to say that a causal relationship exists when we see a trend line with lots of tight fitting dots around and two different variables on an X and Y axis, but this can be misleading.
In The Book of Why Judea Pearl writes, “Regression coefficients, whether adjusted or not, are only statistical trends, conveying no causal information in themselves.” It is easy to forget this, even if you have had a statistics class and know that correlation does not imply causation. Humans are pattern recognition machines, but we go a step beyond simply recognizing a pattern, we instantly set about trying to understand what is causing the pattern. However, our regression coefficients and scatter plots don’t always hold clear causal information. Quite often there is a third hidden variable that cannot be measured directly that is influencing the relationship we discover in our regression coefficients.
Pearl continues, “sometimes a regression coefficient represents a causal effect, and sometimes it does not – and you can’t rely on the data alone to tell you the difference.” Imagine a graph with a regression line running through a plot of force applied by a hydraulic press and fracture rates for ceramic mugs. One axis may be pressure, and the other axis may be thickness of the ceramic mug. The individual points represent the point at which individual mugs fractured We would be able to generate a regression line by testing the fracture strength of mugs of different thickness, and from this line we would be able to develop pretty solid causal inferences about thickness and fracture rates. A clear causal link could be identified by the regression coefficients in this scenario.
However, we could also imagine a graph that plotted murder rates in European cities and the spread of Christianity. With one axis being the number of years a city has had a Catholic bishop and the other axis being the number of murders, we may find that murders decrease the longer a city has had a bishop.  From this, we might be tempted to say that Christianity (particularly the location of a Bishop in a town) reduces murder. But what would we point to as the causal mechanism? Would it be religious beliefs adopted by people interacting with the church? Would it be that marriage rules that limited polygamy ensured more men found wives and became less murderous as a result? Would it be that some divinity smiled upon the praying people and made them to be less murderous? A regression like the one I described above wouldn’t tell us anything about the causal mechanism in effect in this instance. Our causal-thinking minds, however, would still generate causal hypothesis, some of which would be reasonable but others less so (this example comes from the wonderful The WEIRDest People in the World by Joseph Henrich).
Regression coefficients can be helpful, but they are less helpful when we cannot understand the causal mechanisms at play. Understanding the causal mechanisms can help us better understand the relationship represented by the regression coefficients, but the coefficient itself only represents a relationship, not a causal structure. Approaching data and looking for trends doesn’t help us generate useful information. We must first have a sense of a potential causal mechanism, then examine the data to see if our proposed causal mechanism has support or not. This is how we can use data and find support for causal hypothesis within regression coefficients.
The Screening-Off Effect

The Screening-Off Effect

Sometimes to our great benefit, and sometimes to our detriment, humans like to put things into categories – at least Western, Educated, Industrialized, Rich, Democratic (WEIRD) people do. We break things into component parts and categorize each part as belonging to a category of thing. We do this with things like planets, animals, and players within sports. We like established categories and dislike when our categorization changes. This ability has greatly helped us in science and strategic planning, allowing our species to do incredible things and learn crucial lessons about the world. What is remarkable about this ability is how natural and easy it is for us, but how hard it is to explain or program into a machine.
One component of this remarkable ability is referred to as the screening-off effect by Judea Pearl in The Book of Why. Pearl writes, “how do we decide which information to disregard, when every new piece of information changes the boundary between the relevant and the irrelevant? For humans, this understanding comes naturally. Even three-year-old toddlers understand the screening-off effect, though they don’t have a name for it. … But machines do not have this instinct, which is one reason that we equip them with causal diagrams.”
From a young age we know what information is the most important and what information we can ignore. We intuitively have a good sense for when we should seek out more information and when we have enough to make a decision (although sometimes we don’t follow this intuitive sense). We know there is always more information out there, but don’t have time to seek out every piece of information possible. Luckily, the screening-off effect helps us know when to stop and makes decision-making possible for us.
Beyond knowing when to stop, the screening-off effect helps us know when to ignore irrelevant information. The price of tea in China isn’t a relevant factor for us when deciding what time to wake up the next morning. We recognize that there are no meaningful causal pathways between the price of tea and the best time for us to wake up. This causal insight, however, doesn’t exist for machines that are only programmed with the specific statistics we build into them. We specifically have to code a causal pathway that doesn’t include the price of tea in China for a machine to know that it can ignore that information. The screening-off effect, Pearl explains, is part of what allows humans to think causally. In cutting edge science there are many factors we wouldn’t think to screen out that may impact the results of scientific experiments, but for the most part, we know what can be ignored and can look at the world around us through a causal lens because we know what is and is not important.
A Vaccine for Lies and Falsehoods

A Vaccine for Lies and Falsehoods

Vaccines are on everyone’s mind this year as we hope to move forward from the Coronavirus Pandemic, and I cannot help but think about today’s quote from Quassim Cassam’s book Vices of the Mind through a vaccine lens. While writing about ways to build and maintain epistemic virtues Cassam writes, “only the inculcation and cultivation of the ability to distinguish truth from lies can prevent our knowledge from being undermined by malevolent individuals and organizations that peddle falsehoods for their own political or economic ends.” In other words, there is no vaccine for lies and falsehoods, only the hard work of building the skills to recognize truth, narrative, and outright lies.
I am also reminded of a saying that Steven Pinker included in his book Enlightenment Now, “any jackass can knock down a barn, but it takes a carpenter to build one.” This quote comes to mind when I think about Cassam’s quote because building knowledge is hard, but spreading falsehoods is easy. Epistemic vices are easy, but epistemic virtues are hard.
Anyone can be closed-minded, anyone can use lies to try to better their own position, and anyone can be tricked by wishful thinking. It takes effort and concentration to be open-minded yet not gullible, to identify and counter lies, and to create and transmit knowledge for use by other people. The vast knowledge bases that humanity has built has taken years to develop, to weed out the inaccuracies, and to painstakingly hone in on ever more precise and accurate understandings of the universe. All this knowledge and information has taken incredible amounts of hard work by people dedicated to building such knowledge.
But any jackass can knock it all down. Anyone can come along and attack science, attack knowledge, spread misinformation and deliberately use disinformation to confuse and mislead people. Being an epistemic carpenter and building knowledge is hard, but being a conman and acting epistemically malevolent is easy.
The task for all of us is to think critically about our knowledge, about the systems and structures that have facilitated our knowledge growth and development as a species over time, and to do what we can to be more epistemically virtuous. Only by working hard to identify truth, to improve systems for creating accurate information, and to enhance knowledge highways to help people learn and transmit knowledge effectively can we continue to move forward. At any point we can chose to throw sand in the gears of knowledge, bringing the whole system down, or we can find ways to make it harder to gum up the knowledge machinery we have built. We must do the latter if we want to continue to grow, develop, and live peacefully rather than at the mercy of the epistemically malevolent. After all, there is no vaccine to cure us from lies and falsehoods.
Improve Your Posture - Joe Abittan - Vices Of The Mind - Cassam

Improve Your Posture

In the book Vices of  the Mind, Quassim Cassam compares our thinking to our physical posture. Parents, physical therapists, and human resources departments all know the importance of good physical posture. Strengthening your core, lifting from your legs and not your back, and having your computer monitor at an appropriate height is important if you are going to avoid physical injuries and costly medical care to relive your pain. But have you ever thought about your epistemic posture?
Your epistemic posture can be thought of in a similar manner as your physical posture. Are you paying attention to the right things, are you practicing good focus, and are you working on being open-minded? Having good epistemic posture will mean that you are thinking in a way that is the most conducive to knowledge generation. Just as poor physical posture can result in injuries, poor epistemic posture can result in knowledge injuries (at least if you want to consider a lack of knowledge and information an injury).
Cassam writes, “The importance of one’s physical posture in doing physical work is widely recognized. The importance of one’s epistemic posture in doing epistemic work is not. Poor physical posture causes all manner of physical problems, and a poor epistemic posture causes all manner of intellectual problems. So the best advice to the epistemically insouciant and intellectually arrogant is: improve your posture.”
Improving our epistemic posture is not easy. Its not something we just wake up and decide we can do on our own, just as we can’t improve our walking form, the way we lift boxes, or easily adjust our workspace to be the most ergonomic all on our own. We need coaches, teachers, and therapists to help us see where we are going through dangerous, harmful, or imbalanced motions, and we need them to help correct us. These are skills that should be taught from a young age (both physically and epistemically) to help us understand how to adopt good habits maintain a healthy posture throughout life.
Thinking in ways that build and enhance our knowledge is important. It is important that we learn to be open-minded, that we learn how not to be arrogant, and that we learn that our opinions and perspectives are limited. The more we practice good epistemic posture the better we can be at recognizing when we have enough information to make important decisions and when we are making decisions without sufficient information. It can help us avoid spreading misinformation and disinformation, and can help us avoid harmful conspiracy theories or motivated reasoning. Good epistemic posture will help us have strong and resilient minds, just as good physical posture will help us have strong and resilient bodies.
Who is Harmed by Epistemic Malevolence

Who is Harmed by Epistemic Malevolence?

One of the reasons we should care about epistemic vices is that they harm all of society. Epistemic vices are vices that hinder knowledge, and since we live in complex and interconnected societies, we rely on shared and easily accessible knowledge in order for any of us to survive. When knowledge is hindered, the chance that complex systems can break down and harm people increases.
 
This idea is important and helpful when we think about our own potential epistemic vices. Our attitudes, behaviors, and actions that hinder knowledge may not harm us, but may harm someone else or may harm broader segments of society. In his book Vices of the Mind Quassaim Cassam demonstrates this by examining epistemic malevolence. He writes, “the person who is deprived of knowledge by the vice of epistemic malevolence is not the person with the vice.”
 
If someone is intentionally misleading you by giving you false information or making you question legitimate information for their own gain, then they are not harmed. They likely know that the information they are presenting and sharing is inaccurate, but stand to gain from you having inaccurate information. They may stand to profit, which motivates their epistemic malevolence, while you are harmed.
 
In some epistemic vices, the individual with the vice is the one who is harmed. Wishful thinkers and gullible individuals are the ones who are harmed by their epistemic vices. However, other epistemic vices, as the malevolence example demonstrates, harm other people. Knowledge is something that is shared and built communally. Few of us develop real knowledge completely on our own, and the power of knowledge is magnified when shared with others. Often, when we get in the way of this process, it is not just ourselves that are harmed, but all of society, increasing the responsibility that we all have to minimize epistemic vices.

Using Misinformation and Disinformation for Political Purposes

Using Misinformation & Disinformation for Political Purposes

“A relentless barrage of misleading pronouncements about a given subject,” writes Quassim Cassam in Vices of the Mind, “can deprive one of one’s prior knowledge of that subject by muddying the waters and making one mistrust one’s own judgement.”
This sentence seems to perfectly describe the four year presidency of Donald Trump. The former President of the United States said a lot things that could not possibly be true, and didn’t seem to care whether his statements were accurate or inaccurate. There were times when he was clearly trying to mislead the nation, and times when he simply didn’t seem to know what he was talking about and made up claims that sounded good in the moment. Regardless of whether he was trying to deliberately mislead the public or not, his statements often had the same effect. They often created confusion, a buzz around a particular topic, and a dizzying array of rebuttals, of support arguments, and complicated fact-checks.
The President’s epistemic insouciance created confusion and bitter arguments that the President could spin for his own political gain. He would lie about meaningless topics and then criticize people for focusing on narrow and unimportant falsehoods. He would say random and sometimes contradictory things which would create so much confusion around a topic that people had trouble understanding what the argument was about and began to doubt factual information and reporting. The result was a blurring of the lines between reputable and fact-based reporting and hyperbolic opinionated reporting.
A clear lesson from Trump’s presidency is that we need to do a better job of holding elected officials to a higher standard with their statements. Unfortunately, it often goes against our self or group interest to hold the elected officials we favor to high standards. If we generally like a politician who happens to be epistemically insouciant, it is hard to vote against them, even if we know what they say is wrong or deliberately misleading. As many of Trump’s supporters demonstrated, it can be more comfortable to do complex mental gymnastics to make excuses for obviously inept and dangerous behaviors than to admit that our favored politician is lazy and incompetent. 
Knowledge and accurate beliefs are important. We have entered a period in humanity where we depend on complex systems. Whether it is infrastructure, supply chains, or human impacts on climate, our actions and behaviors are part of large interconnected systems. None of us can understand these systems individually, and we depend on experts who can help us make sense of how we relate to larger wholes. We need to be investing in and developing systems and structures that encourage and facilitate knowledge. Using misinformation and disinformation for political purposes inhibits knowledge, and makes us more vulnerable to system collapses when we cannot effectively and efficiently coordinate our actions and behaviors as complex systems change or break. Going forward, we have to find a way to prevent the epistemically insouciant from muddying the waters and clouding our knowledge.
Epistemically Malevolent & Epistemically Insouciant

Epistemically Malevolent & Epistemically Insouciant

Over the last few years I feel as though I have seen an increase in the number of news outlets and reporters saying that we now live in a post-truth society. The argument is that truth and accuracy no longer matter to many people, and that we live in a world where people simply want to believe what they want to believe, regardless of the evidence. This argument is supported by documented instances of fake news, by a former US president who didn’t seem to care what the truth was, and by politicians and every day people professing beliefs that are clearly inaccurate as a type of loyalty test. This puts us in a position where it becomes difficult to communicate important information and create a coherent narrative based on accurate details surrounding the events of our lives.
Two concepts that Quassim Cassam discusses in his book Vices of the Mind can help us think about what it means to be in a post-truth society. Cassam writes, “one can be epistemically malevolent without being epistemically insouciant.” To me, it seems that a post-truth society depends on both malevolency and insouciance to exist. I find it helpful to see that there is a distinction in these two different postures toward knowledge.
To be epistemically malevolent means to intentionally and deliberately attempt to hinder and limit knowledge. Cassam uses the example of tobacco companies deliberately misleading the public on the dangers of smoking. Company executives intentionally made efforts to hide accurate scientific information and to mislead the public. In recent years we have seen epistemic malevolence in the form of fake-news, misinformation, and disinformation intended to harm political opponents and discourage voter turnout for opposing political parties.
Epistemic insouciance doesn’t necessarily have a malicious intent behind it. Instead, it is characterized by an indifference to the accuracy of information. You don’t need an intentional desire to spread false information in order to be epistemically insouciant. However, this careless attitude toward the accuracy of information is in some ways necessary for false information to take hold. Individuals who care whether their knowledge and statements are correct are less likely to be pulled in by the epistemically malevolent, and less likely to spread their messages. However, someone who favors what the epistemically malevolent have to say and is unwilling to be critical of the message are more likely to engage with such false messaging and to echo and spread malevolent lies. Even if an individual doesn’t want to be intentionally misleading, insouciance plays into malevolence.
This helps us see that our post-truth society will need to be addressed on two fronts. First, we need to understand why people are epistemically insouciant and find ways to encourage people to be more concerned with the accuracy and factuality of their statements and beliefs. External nudges, social pressures, and other feedback should be developed to promote factual statements and to hinder epistemic insouciance. This is crucial to getting people to both recognize and denounce epistemic malevolency. Once people care about the accuracy of their beliefs and statements, we can increase the costs of deliberately spreading false information. As things exist now, epistemic insouciance encourages epistemic malevolency. Combating epistemic malevolency will require that we address epistemic insouciance and then turn our attention to stopping the spread of deliberate falsehoods and fake news.