Do people care who (or what) wrote this?

Generative A.I. as a writing tool has limitations. But what I've discovered over the past week is that my perceptions of those limitations can drastically change when I learn about a new way to use it. Before, I'd been giving ChatGPT fairly vague prompts: "Describe the town of Cottondale Alabama as a news article." Listening to a copy.ai prompt engineer on Freakonomics helped me understand that being more specific in your prompts about the length of the output ("500-1000 words") and the audience ("highly-educated audience") makes all the difference. 

The other key lesson is to think of writing with A.I. as an iterative collaboration: ask the program to generate five options, use your gold ol' fashioned human judgment to select the best one, then ask it to further refine or develop that option. If you find it to be boring, ask it to vary the sentence structure or generate five new metaphors for something and then pick the best one. I sensed that writing with generative A.I. could be more like a collaboration with a co-author than an enhanced version of auto-correct; this helped me to see what, exactly, that collaboration looks like, and how to effectively collaborate with the program. 

As the output got better and better, I wondered, "has anyone done a blind test of readers' ability to discern A.I.-assisted writing from purely human writing?" I'd heard of a few misleading journalistic stunts where writers trick readers into thinking that they're reading human writing when, in fact, they are not. But I'm looking for something more rigorous, something that compares readers' abilities to discern that difference across genres of writing: short news articles, poetry, short stories, long-form journalism, short documentary scripts, etc. It seems likely that readers will prefer the A.I.-assisted version in some cases, but it's important to know what types of cases those will be. 

I also wondered what our reactions - as readers and writers - to all of this. I can think of three metaphors for possible reactions to A.I.-assisted writing:

1) the word processor. It's use changed how writers write. It changed the output. Like most disruptive technologies, it was met with skepticism and hostility. But eventually, it was widely adopted. Young writers who hadn't grown up writing free-hand had an easier time adapting to this new way of writing. The technology became "domesticated" - normal to the point of being invisible, embedded in pre-existing structures of economy and society. 

2) machine generated art. Machines have been generating visual art for decades. Some of that art is indiscernible from human generated visual art. Some of it embodies the kinds of aesthetic characteristics that people value. And yet machine generated art has never risen beyond a small niche. The market for visual art largely rejects it, in part because those who enjoy art care about how it is created. Something about the person who created it and the process by which it was created is part of what they value about art. 

3) performance enhancing drugs. Output from A.I.-assisted writing is superior - in some cases far superior - to unaided human writing, and there is market demand for it - the public sets aside its qualms and embraces good writing regardless of how it came about. This situation is perceived by writers, some industries, and some governments as unfair or possibly dangerous, maybe in terms of what bad actors could do with such a tool or how profoundly disruptive its widespread use would be for economies and society. Therefore, they regulate it, discourage its use through public shaming, or, in some cases, explicitly forbid its use. 

The quality of A.I.-assisted writing's output is only part of what will determine its eventual place in our lives. The general public's reaction to it is another part worth paying attention to. 

Experts Worry: Predictive News Headlines in the Age of COVID-19

Cataclysms, personal or shared, have a way of distorting your perception of time.

March 12th, 2020 was the day that I felt the Coronavirus crisis escalate. That day, I felt as though I was living simultaneously in three distinct realities. First and most vividly, there was the physical world around me. It was one of those blissful early spring days - bright and breezy. Some friends were meeting up at the local watering hole for a happy hour that was tinged with a different energy than previous ones. I think we knew it might be the last time we'd see each other in person for awhile, but this didn't make us glum. Instead, there was a kind of enhanced camaraderie, laughing at the craziness of it all, because what else was there to do?

Then, existing in what seemed like another universe, there was the reality that existed inside my computer and phone, on news websites and social media: a world quickly falling apart. There was an ever-escalating series of fear-evoking stories, every one of them true.

And then there was the thing itself: the unknowable reality of threat posed by the virus. Though the virus itself is knowable insofar as we are able to know viruses and what they do to various types of human bodies, the matter of immediate concern - the precise way the virus will spread through a given population and affect each individual person - could not be known. That reality is contingent upon too many things to be knowable, at least in mid-March, and perhaps now in early May and for the foreseeable future. The threat of the virus depends on future government policies at federal, state, and local levels, future workplace policies, the speed with which treatments will be developed and manufactured, and the future behavior of billions of individuals, as each individual's decision to, say, stay home and watch Netflix or say 'fuck it' and go out to a bar (when bars were/will be open) affects all downstream outcomes. Scientists of various stripes can imperfectly predict the spread of the virus and its deadliness by looking at various models based on prior behavior, but they cannot as yet know with absolute certainty (or really much certainty at all, it would seem) who the virus will infect in a particular social context, when it will infect them, how long it will stick around a population, who or how many it will kill.

As I moved into the gently mandated quarantine stage of the event, I started to think more about the disjuncture among these three worlds. A couple of months previous, I'd had time to think about the ways in which the social internet (news, commentary about current events on social media) presented its users with a distorted view of the world. The long and short of it is this: it enhances threats.

In the case of the virus, the threats are multiple. There is the virus itself, and then there is the effect of virus containment on the economy. Also, as always in the U.S. and perhaps elsewhere, there is the threat of the other political tribe: Would Trump invoke martial law? Would protesters spread the virus? Would liberals exaggerate the threat to make Trump look bad?

The key word in all of these questions is 'would.' I came to realize that many of the headlines I read contained words like 'would,' 'could,' 'may.' Some of the bolder ones contained the word 'will.'

They were about bad things that had not happened yet. 

On a podcast from mid-March, Malcolm Gladwell recounted something he had read that day that quoted experts from the University of California San Francisco, a leading medical school. The experts predicted that there would be more than 1 million Americans deaths from the virus.

From March 17, 2020 on the New York Times: 'There may be two to four more rounds of social distancing.'

And later, from CNN.com on April 14th, 2020: 'US may have to keep social distancing until 2022, scientists predict.'

From May 4th in the Washington Post: 'Draft report predicts covid-19 cases will reach 200,000 a day by June 1.' This article alluded to a leaked report from the CDC that also predicted that there will be 3,000 deaths per day in the second half of May.

These headlines were accompanied by opinion-piece headlines that would have seemed more at home on less prestigious news websites a few months ago. From the New York Times: 'One simple idea explains why the economy is in great danger'; 'Stop saying everything is under control. It isn't'.' 'More severe than the great recession.'

Most of the headlines and stories quoted experts who offered informed predictions about the virus or the economy. Right away, I thought of Phil Tetlock's work on expert political judgment. Tetlock found that when political experts of all ideological stripes were forced to make falsifiable predictions about a range of outcomes, they were not much better than chance or non-experts. The more famous the experts were, the worse their prediction records were. In his book The Signal and the Noise, Nate Silver reviews the incentives experts have to make predictions, why they are rewarded for more outrageous predictions with more coverage and fame, and why they are not punished for being wrong.

There are various lessons you might take away from Tetlock's ongoing project to assess the ability of experts to forecast a range of outcomes. The one I keep coming back to is that forecasting any outcomes that involve a large number of people's behavior is really difficult. The more people that may influence the outcome (that is, the more people who's individual behavior is part of the system you're trying to observe and extrapolate from), the harder the outcome is to predict.

Some of the predictions about the virus and the economy that dominated headlines were not falsifiable, as they did not provide a time range and thus could eventually be proven true even if they were not true on a particular date (they would never be false, but simply not true yet). But many were falsifiable: they made specific predictions about the duration or magnitude of an economic recession or depression; they made specific predictions about the number of infections or deaths resulting from the virus (the falsifiability of which assumes you accept data collected by authorities).

Sometimes, the experts would try to convey their levels of uncertainty in their predictions. Sometimes, they would not. Most times, this uncertainty level would not be conveyed in the news article; it was almost never conveyed in the headlines.

Aside from the inherent unpredictability of large groups of people, there is another reason why many forecasts relating to the virus or the economy will turn out to be wrong. Predictions like this can function as warnings that are then heeded by people who take action, which prevents the predicted outcome from occurring. Nate Silver calls this a 'self-cancelling prediction' or 'self-cancelling prophecy.' Similar problems plague predictions about environmental catastrophe: the more dire the predictions, the more likely they are to spur innovation or regulation that prevents the predictions from coming true.

I wonder if some folks are engaging in a kind of deliberately misleading, exaggerating framing of virus threats. Perhaps journalists and those who post on social media are aware of the shortcomings of the data they are working with, aware that they are focusing on the most dire scenarios and ignoring others. They do this because they believe, rightly, that the more dire the predictions, the more likely they will be to spur action that will prevent the more dire predictions from coming true.

I think that people often derive the wrong lesson from self-cancelling predictions. They do not prove the predictions to have been correct. It is possible that the prediction would have been wrong had no action been taken. In and of themselves, they do not provide much evidence of the accuracy of the prediction. I think the better lesson to draw from the possibility of self-cancelling predictions is that in order to have faith in our predictions in which those who learn of the prediction might plausibly affect its outcome, we must understand the mechanisms by which the predicted outcome will or won't occur. We must be able to account for the effects of particular behaviors in isolation (e.g., the effect of social distancing on viral transmission; the effect of carbon monoxide on sea levels) in order to really understand and predict complex phenomena.

The recent spate of predictive headlines brings to mind another domain examined in Nate Silver's book: weather predictions. Meteorologists' predictions of the weather on any given day were often wrong; no surprise there, as weather is another complex, hard-to-predict system. What's interesting is that the errors were systematic: meteorologists tended to predict rain on days that turned out to be sunny more often than they predicted sun on days that turned out to be rainy. As a reason for this, Silver noted that meteorologists were 'punished' for one type of wrong answer more severely than they were for the other. Most people saw sun on a supposedly rainy day as a pleasant surprise, while they tended to get angry at meteorologists who failed to warn them about the negative outcome: rain on a supposedly sunny day.

Many of the predictions dominating news headlines will inevitably turn out to be wrong, but will they be systematically wrong, wrong in a particular direction? I suspect that most journalists and news consumers see virus infections, deaths, and economic hardship in much the same way people see rain: they would rather the predictions turn out to have been too dire than not dire enough.

However, this creates a problem. If the predictions about virus infections and deaths are unnecessarily dire, this will cause consumers to spend less and investors to invest less, leading to worse economic outcomes. If the predictions about the economy are too dire, policy makers, business owners, voters, and consumers will push for re-opening too soon, resulting in worse health outcomes. All unnecessarily dire predictions will likely harm people's mental and emotional health, and any wrong prediction will harm subsequent trust in news sources.

I suspect that predictions in most mainstream news outlets will overestimate negative outcomes associated with the virus and underestimate negative outcomes associated with the economy. Of course, there's a political element to the predictions (those on the Left tend to be more concerned with the virus while those on the Right tend to be more concerned with the economy), but beyond that, I think the negative outcomes associated with the virus (mass death; dying alone) are more vivid, more viscerally repellent than those associated with the economy (lagged rises in social unrest, substance abuse, domestic abuse, and violent crime that typically accompany prolonged mass unemployment) which tend to be more diffuse and less easily depicted.

What to do about all this? Well, before going any further, it seems worthwhile to test all of my assumptions. In the spirit of putting my money where my mouth is, here are a few falsifiable hypotheses:

• The number of 'predictive headlines' (i.e., headlines that relay information about an event or state of the world that has yet to occur at the time of publication) has increased since the middle of March 2020. 

• Of the predictive headlines that are falsifiable at present, more headlines will have overestimated threats than will have correctly estimated or underestimated threats. 

• The more vivid the threat, the greater the magnitude of the error in prediction. 

• News consumers exposed to more dire predictions will be more likely to take action (or intend to take action) than those exposed to less dire predictions. 

• The inclusion of information about the confidence levels of experts (e.g., swapping out the word 'will' for the word 'could' or 'might') will have no effect on news consumers' behaviors or intentions. 

To motivate journalists and those who post on social media not to post speculative 'news,' perhaps we could shame the behavior with a catchy, albeit misleadingly reductive moniker: 'eventually fake news,' or something like that.

I can understand the desire to compulsively speculate at a time like this. Typically, there's a certain amount of uncertainty in the world. You might not know some of the details about what will happen over the next year, but you often have a rough idea of what it will be like, what you'll do, where you'll be. At a time when so much is uncertain, maybe we can't help ourselves from making predictions, even if we know most of them will turn out to be wrong. But even in times of great uncertainty, there must be something we can learn from our wrongness. Right?

What's Wrong with News?

Since Jimmy Wales came to speak at the University of Alabama last weekend, I've been thinking more about WikiTribune, his new-ish news curation/creation venture. This seems like a rare opportunity to contribute to the building of a tool that has the backing of the creator of one of the most popular, influential, important websites in the world. Of course the venture might fail, because most new ventures fail and because news is a tricky thing to get right, maybe trickier in a lot of ways than building something akin to a library or encyclopedia. WikiTribune seems to be functioning right now as a collectively curated news aggregation website; it might evolve into something else in the future, but I'll assume that's what it is for now.

What might be the starting points for a platform like this? If news is broken (which seems a fairly uncontroversial take in 2019), what part of it could be fixed by a Wiki-type platform?

First, there's the issue of factual accuracy. A decent way of getting a newsfeed that only has news that is factually correct is just by linking to stories that are from sources that can be held accountable, that have a reputation, and that typically follow standard journalistic practices. You weed out the parody stories, the polarizing disinformation, the deliberate attempts to poison discourse with fake news. This seems like something a dedicated group of volunteers could do.

I feel like the factual accuracy problem isn't as widespread as some believe, that networks of upvoting and sharing bots only make it seem as though a handful of untrue stories are being very widely read and believed. Of course, there are most likely relatively small pockets of people who are actually believing in false stories and act on those beliefs, and despite their relatively small numbers, they can be very harmful to society. And various characteristics of popular social media platforms such as Twitter, YouTube, and Facebook (such as algorithms that don't vet information, the ability for anyone to post and share anything, being too big and too fast to moderate) increase the footprint and influence of false stories beyond what they were in the pre-internet says.

I doubt WikiTribune would lure the type of folks who seek out false stories away from their favorite hyperpartisan sites, but who knows what might happen if it eventually became even a tenth as popular as Wikipedia. Maybe it would end up as a kind of standard system for current events information vetting, a better middle layer between journalism and audiences than social media currently is. Most people would then recognize the false news stories and sources in a way that most people did before the internet: as a kind of inevitable fringe to the information ecosphere, relegated to a recognizable outskirt rather than popping up in the midst of a feed of journalism vetted in the traditional way (as tends to happen w/ news via social media). It's important to know whether factually inaccurate news exists, how much of it exists, and who is sharing and reading it, but it's also important to think about where, in our information environment, it resides. Is it concentrated or distributed? In the center or on the periphery?

During his talk, Wales brought up the problem of clickbait headlines: headlines that mislead audiences, play on our emotional, tribal, impulsive tendencies, exploit a kind of shallow curiosity. So, maybe WikiTribune curators use a kind of rough guiding principle: avoid posting clickbait headlines (or maybe just rewrite the headlines. Often, the stories are fine, but the headlines seem like they were written by search engine optimizers). Obviously, clickbait is a term with a fuzzy definition, but that's nothing new, and certainly doesn't stop websites, publishers, etc. from moderating various kinds of vaguely-defined content standard. Get multiple experienced coders to rate each headline's clickbait-iness, and when they agree, don't post it or re-write the headline.

Wales also brought up ads and their effects on content, one of which is to essentially incentivize the creation of clickbait headlines. But I guess I'm a bit unclear on how to account for the fact that many of the sources to which WikiTribune links are ad supported. Related to this, how does WikiTribune work with paywall news sites? Wales seemed to be pro-paywall, to endorse the idea that if more news sites were subscription-funded rather than ad-funded, the quality of information would improve. Would WikiTribune just give you a taste of the article, a kind of abstract or summary, something a bit more than a headline, some kind of compromise that would give the reader some value while not totally substituting for the story itself, perhaps pushing users to the full article in the way that Wikipedia might push users to the source material? That seems reasonable to me.

He also brought up algorithms. Perhaps algorithms also, in some way, guide news consumers and creators toward more clickbait-y headlines. If you have humans in the loop, maybe it would be easier to slow or stop the prevalence of clickbait and false news.

He also brought up the fact that Wikipedia is not totally open and it's not a democracy, and I think that's a way of setting this apart from the primary news aggregator of our time, Reddit. Reddit is ostensibly open, and is a kind of democracy: registered users submit and vote on content, thereby increasing or decreasing its visibility. Over time, this has led to certain kinds of news stories ending up on the front page. You might call it a product of 'hivemind.' It has a certain narrow tonal range, and a certain focus on particular topics that reflects the interests and values of the voting public.

There is recent talk of moderators playing a bigger role in directing what gets posted in subreddits, and the inevitable push-back by those who value unfettered speech and a democratic public sphere above all else. I doubt that push-back will fully subside on Reddit because voting for the noteworthiness of content is a defining characteristic of the platform. It's not ancillary in the way that 'likes' are to Instagram. But if you start a new site that simply doesn't have that as one of it's defining characteristics, you don't necessarily have that problem. Sure, a lot of people believe strongly in a flat hierarchy, an open democratic sphere, but I think that enough people have been repulsed by what that yielded to want something else.

Somewhat more controversially, we might consider the question of tone and emotion in news, and whether or not that needs 'fixing,' or could be fixed. Personally, I'm turned off by the abundance of outrage and fear that many news stories in 2019 evoke, but I recognize that an argument can be (and has been) made for the value of anger and fear in this sphere. Here, I think it's more a matter of personal preference, and not everyone would agree on this, but some would like, sometimes, to see a newsfeed that features less fear and anger. I feel like that's where a lot of popular democratic news feeds, like r/news on Reddit, end up: a kind of distilled outrage and fear.

Here, we need think about the purpose of news. Is it a kind of 'immune system' for societies that's sole purpose is to detect threats and alert us to them? Well then, it seems entirely appropriate that news would invoke anger and fear. Should news be more broad, emotionally, than that? Should it invoke wonder, curiosity, gratitude? You have subreddits like r/upliftingnews that cater to another point on the emotional spectrum, so it's not as if there isn't already a place for that in many people's information diets.

Then there's the matter of filter bubbles/echo chambers, and I don't know that there's much that WikiTribune could do about that. I think worries about filter bubbles and echo chambers are somewhat overstated and/or that we'll never fully solve that problem, and while we wait around for the perfect solution, we're making do with a pretty lousy news ecosystem that's run, by default, by impulsive clicks and a lack of accountability. I think pure democracy was one potential solution - giving the power of vetting and curation to anyone and everyone - but we've seen how well that went.

Wikipedia never solved the filter bubble problem when it came to creating an encyclopedia. The editors don't remotely reflect the readership, in terms of race, gender, education level, ideology, etc. Wikipedia isn't flawless, but it seems to be working well enough, and I gather that there is a sense within the organization that they should try to broaden the diversity of people who edit it to include more women and people of color. Should it also try to include more people who identify as politically conservative? Does it make sense to pursue intellectual diversity among information curators as well?

It looks like the current version of WikiTribune features a way to follow particular feeds curator by particular Wiki-editors. If that's the case, then what's to stop a couple of ultra-liberal or ultra-conservative editors from setting up feeds full of clickbait and partisan vitriol? Is there some overseer that decides when editors have gone too far, similar to the way that admins on Reddit ultimately have control over volunteer moderators? Might that decision as to what goes too far be motivated by one's ideology?

Yes. But I get the sense that Wikipedia has already dealt with similarly motivated people who have tried to turn Wikipedia into a more partisan information environment, and it has some sort of mechanism for dealing with them that seems to be largely effective. Is the mechanism entirely democratic and open? Probably not, but now might be the time that some of the public revisits the relationship between direct democracy and news curation and distribution.

Even if something similar to WikiTribune existed in the past (and I get the sense that that's the case) and ultimately failed, that does not determine whether WikiTribune will fail. There are many, many cases in which a creation didn't succeed because it was timed poorly. Maybe we had to wait to see how poorly open, democratic, free-for-all, algorithmic, impulsive curation of news would go before there would enough demand for something like WikiTribune to be sustainable. I'm just happy to see someone trying something like this right now.

A World Without Frames

I had the privilege of seeing Jimmy Wales, founder of Wikipedia, speak at the University of Alabama, courtesy of The Blackburn Institute. In anticipation of seeing him speak, I'd been reflecting on the value of Wikipedia in a post-2016 world. Since the 2016 U.S. Presidential election, it seems as though the discourse on social media has become more toxic and less fact-based, that traditional news outlets don't quite know what to do with Trump and his international equivalents, and that non-traditional news outlets are creating and disseminating biased and false information about our world. All the while, Wikipedia continues to churn away, largely free from the toxicity and contentiousness that has gripped the rest of the internet and, seemingly, the rest of the world. How did they do it?

I went into the talk with a slightly more specific version of that question: is it the particular approach/model that Wikipedia uses that is responsible for its relative success vis a vis the Truth, or is it the particular domain in which it operates - that of the encyclopedia? Wales didn't quite speak to this question, but he did talk about his relatively new passion project: the WikiTribune. In a way, the fate of that venture will answer the question, as it applies the model and logic of Wikipedia to the world of news and current events. Wales' working theory seemed to be that ads were largely to blame for the degradation of news: the way that the online ad economy works puts all websites on an even playing field, all competing against one another for attention. News sites do not just compete with other news sites; they compete with parody news sites, entertainment sites, gossip sites, etc. He praised the recent move toward the subscription model, noting that the New York Times has seen recent financial success pursuing this model. Subscriptions prompt users to think, 'what is the overall value of this product, in the long term?' That's a key shift in thinking, from what you click on in an impulsive manner to what you value, a shift from short term thinking to long term thinking. So, if we get rid of the ads, do we improve the quality of news and discourse around news?

My suspicion is that there is another factor at play: whether the content pertains exclusively to current events. News must privilege certain stories over others in a way that an encyclopedia or a library does not, assuming it has a front page (can we conceive of a news site without a front page, regardless of whether said front page is personalized?). Here, the decades of research on framing and agenda setting is relevant: by virtue of editorial decisions about what to cover and what not to cover, news gets us to think about certain issues or events (or certain aspects of those issues/events) and ignore others. Encyclopedias do not direct attention in quite the same way. Sure, it could be argued that within a given entry, an encyclopedia/wikipedia chooses to emphasize certain aspects of the subject while ignoring or downplaying others, and thus frames the subject in a way that shapes perception. But I'd argue the way in which Wikipedia has incorporated different perspectives on contentious topics into entries reduces this effect.

Can you do the same thing with news? Is that what WikiTribune will be?

I haven't a clue. But it was kind of thrilling to be in the room with someone who was taking a stab at solving a problem of this scope, someone who was uniquely positioned to stand a decent chance of succeeding.

The Circulation of Trauma

Teaching a class on images of war has been an education for me. It is not my area of expertise, which perhaps made it all the more fascinating. I've had the opportunity to meet many wonderful guest speakers, think about things I don't normally think about, and make new connections when thinking about the subjects that I spend most of my time thinking about.

In our most recent class, we screened the powerful documentary Under Fire, which concerned post-traumatic stress disorder among war journalists. After the screening, we spoke with one of its producers, neuropsychiatrist Dr. Anthony Feinstein, and one of the journalists depicted in the film, Pulitzer Prize winner Paul Watson. Both were as candid and thoughtful as people who encounter trauma could be.

Watson's experiences as a war journalist are the stuff of classic tragedy. His telling of his experience being the photographer who helped change American military policy through his photos of dead American soldiers in Mogadishu in 1993 is the emotional climax of the documentary. It is also the basis of a series of poems, a play, and an opera that will be performed in 2014 in NYC. I've always felt a bit queasy about turning one person's suffering into another person's art/entertainment. Maybe it's the commodification that bugs me, the building of a career on the suffering of others. Even if the message affects others in some positive way, that other exploitative layer is there. Watson's pain also seems incredibly personal and private. In some respects, the troubles with the use of Watson's emotional and mental trauma for the purposes of art mirrors the trouble at the heart of his story: the depiction of death and suffering as a kind of desecration, no matter how noble the intentions. One idea I was left to mull over after that evening was how we can discuss trauma without it overwhelming us, or infecting us.

This relates to my musings on the circulation of ideas and stories about personal suffering online. When do our consumption of these ideas and stories and the conversations they give rise to provide us or the sufferer with solace? When do they prompt us to dwell on the suffering? When do they help us work through the suffering? These are "difficult" topics to think about, to speak about, to hear about. Watson had the feeling that there was something fundamentally wrong about recording and distributing these horrible images. Another part of him knew that they were important for the world to see.

Watson knew that some images he saw would overwhelm an audience. When he encountered piles of dead children, he didn't photograph them. He depicted the horror obliquely, showing the children's schoolbooks scattered on the ground. That ability to take in some overwhelming horror of real life and to retain it's essence through storytelling is, I'm understanding, an invaluable part of being a reporter, an artist, and maybe being a compassionate, honest human being.