Aaah – another site about The Future of Journalism.

A dull one.  Without the  invective and ideology about free vs. paid, pajama-clad bloggers vs. stick-in-the-mud mainstream media curmudgeons, and Utopian visions of crowdsourced news vs. dark fears about falling standards you can find elsewhere.  It has words like taxonomy and persistent content in it; discusses business models and revenue streams in dull, accountant-like language; and tries to dissect the sparkling prose journalists turn out into tiny bytes of data.

But there is a purpose here, and it’s based around the idea that we as journalists haven’t really thought about how people are changing they’re accessing information, or about how we need to fundamentally rethink the way we carry out journalism and the kinds of – for want of a better word – products we turn out for them.

There’s much hand-wringing over the loss of the traditional business model of news, it’s true.  Perhaps too much.  And this site will contribute its share.  But hopefully it’ll also explore some of the less-explored questions about where the profession goes in a digital age.   And lay out some of the thinking behind one concrete idea that might help move the business forward: Something I’m calling Structured Journalism.

So, welcome – and I hope you find this interesting.

As I mentioned last week, I took leave of my senses and headed to Boston on Thursday to catch up with Bill Adair, Laura and Chris Amico, and David Smydra and discuss what we wanted to achieve in structured journalism this year.

Here’s Bill’s note to the Google Group that Chris set up last year (and please sign up for the group, if you haven’t already):

Fans of structured journalism!

Here’s a report from a planning session we had last week:

We held Friday’s meeting in a windowless conference room because that’s where we go when we discuss structured journalism. This year, our meeting was at Google’s office in Cambridge, Mass., and our host David Smydra upheld the tradition.

It’s not that we fear sunlight. It’s just a quirk of scheduling. You may recall that last year’s meeting, our first, was in a windowless room at Reuters where we munched on cold pizza. This year, we got to enjoy Google’s amazing lunch buffet.

Around the table: David, Reg Chua of Reuters, Laura and Chris Amico, the creators of Homicide Watch, and me.

The goal for our second annual structured journalism strategy session was to assess how we did in our first year and set goals for the next 12 months.

We discussed our successes. We held well-attended panels at the International Journalism Festival in Italy and the Online News Association meeting in Chicago and we started this mailing list. We also wrote blog posts and articles, although we each felt we could have done more.

We discussed our new structured projects: Laura did a demo of a cool one she’s leading at the Boston Globe, which should be published in the next couple of weeks; Chris did a demo of two structured sites he’s built for Frontline, Ballot Watch, which follows the changes in state voting laws, and Ebola Outbreak: How the Virus Spread.

I showed two mock-ups

In the Philip K. Dick short story on which the two (not terribly good) Total Recall movies were based, people can pay to get false memories implanted in them, in lieu of real experiences. We don’t have that technology (yet), but it’s true that we’re increasingly outsourcing the task of remembering our lives to machines.

Is that a good thing? And should journalists care? I vote for yes.

But that’s not necessarily a conclusion of an interesting New York Times piece on Sunday that explores the process of memory as we take thousands upon thousands of photos and videos of our daily experiences.

Are shutterbug parents wiping away their mental databases of experiences with their offspring while bulking up their digital ones? And when children grow up reviewing thousands of pictures and hours of video of their young lives, will these images supersede their memories?

It’s almost certainly true that technology is changing what and how we remember – and we’re certainly losing something of ourselves in that process.  On the other hand, we’re gaining volume and accuracy, and that’s not a small thing.  As the Times piece notes, quoting Linda Henkel, a professor of psychology at Fairfield University:

“It’s like the family stories we tell. There’s the original experience, and then the story everyone tells every Thanksgiving. The story becomes exaggerated, a schema of the original event. The physical photo doesn’t change over time, but the photo becomes the memory.”

Human memory is notoriously fallible – as Brian Williams has discovered, to his cost – because we’re designed to remember meaning, not details. And as our relationship

Call it temporary insanity.

There’s no other way to explain why I’m off to snowy Boston this Thursday, to catch up with Laura and Chris Amico, Bill Adair and David Smydra to talk about structured journalism and what we hope to accomplish this year.

Why we couldn’t do this in somewhere warmer and less snow-bound – like Alaska, say – I don’t know. The first time we did this, we were in a windowless room at Reuters in New York, chomping on cold pizza. But at least it was balmy out.

Nonetheless, this is a good thing.  As I’ve mentioned here, structured journalism had a good 2014, thanks in large part to the energy and drive of Bill, Laura and Chris coming out of that first meeting.  We got a discussion going among the journalistic community, and structured journalism has become enough of a shorthand term that we can get past the what-is-this-thing-anyway part of the conversation and more into the meat of how we might try to see if it can help us.

So what do we want to accomplish in 2015? I’ll be eager to hear what Laura, Chris, Bill and David have to say, of course, and there are a couple of threads I hope we might also pick up, from encouraging more experiments, whether in start-ups or embedded in more traditional newsrooms, to exploring the prospects for monetization, to sharing the often-painful lessons learned in the process of launching projects, to thinking more about CMSes. And just more discussion overall on the pluses and minuses of this approach to news and where new experiments might be appropriate.

Should be fun. But cold.

Should machines try to stamp out discrimination – or should they just work efficiently? And what should journalists do about tracking the answer?

The question comes up as we increasingly turn to algorithms and digital platforms to manage many of the things we used to do offline. Offline, there are any number of laws and practices that regulate behavior. Online – well, that’s a whole new landscape. And one we ought to cover a lot more.

There was an interesting NYT piece from a little while back about start ups that are trying to use data analysis to make lending decisions – not so much looking into people’s credit history, but more throwing together thousands on seemingly unrelated pieces of information to predict borrowing (and repayment) behavior.

No single signal is definitive, but each is a piece in a mosaic, a predictive picture, compiled by collecting an array of information from diverse sources, including household buying habits, bill-paying records and social network connections. It amounts to a digital-age spin on the most basic principle of banking: Know your customer.

Does it make sense that people who capitalize properly are better credit risks than people who don’t? Well, the people who make the software not only don’t care, they’d rather they didn’t even try to understand it.

“It is important to maintain the discipline of not trying to explain too much,” said Max Levchin, chief executive of Affirm (one of the companies profiled). Adding human assumptions, he noted, could introduce bias into the data analysis.

True, it may be great that machine-learning systems that crunch lots of data can find correlations that allow more people more access to credit than under the traditional banking system. But what if it turns out that it’s denying credit to certain groups – not intentionally, but simply because that’s the way the data correlates?

The danger is that with so much data and so much complexity, an automated system is in control. The software could end up discriminating against certain racial or ethnic groups without being programmed to do so.

But if you wanted to program in less discrimination, how would you do it – and how much less would you want, if it wound up being less effective at channeling money to people who need it and aren’t being served by the existing banking system?

Or what if it isn’t the machine, but just a lot of people who, each acting on their own, wind up exhibiting discriminatory behavior en masse?

Consider this much-cited paper by Harvard Business School professors Benjamin Edelman and Michael Luca, who showed that non-Black hosts in New York City manage to charge, on average, 12 percent more than Black hosts on Airbnb, after correcting for location, ratings, quality and so on. In that case it wasn’t so much an algorithm determining prices

How moral should a machine be? How moral can a machine be?

Maybe that conjours up science-fiction images of Hal 9000, Cylons, Skynet and even lovable Robby the Robot.

But what it should really do is make us question the assumptions, values and directions embedded in the multiple algorithms that run our lives – not to mention how, increasingly, we won’t really know what assumptions, values and directions are in them.

Consider this scenario, which NYU cognitive science professor Gary Marcus posited in a 2012 New Yorker article:

Your car is speeding along a bridge at fifty miles per hour when errant school bus carrying forty innocent children crosses its path. Should your car swerve, possibly risking the life of its owner (you), in order to save the children, or keep going, putting all forty kids at risk?

Now, I’m sure we all have different answers to this question, perhaps related to how bratty the 40 kids are. But what if it’s the algorithm that powers a self-driving car that has to make that split-second decision?  What are the factors that should be programmed into that piece of software?

Welcome to the world of machine ethics. Or, as Prof. Marcus notes:

As machines become faster, more intelligent, and more powerful, the need to endow them with a sense of morality becomes more and more urgent.

Or at least the need to debate and discuss the questions about how issues of morality might be incorporated into machines.  Those issues cropped up again in this weekend’s New York Times Magazine, which raised similar questions about machine-delivered medical care and semi-autonomous weapons systems. If machines can quasi-independently choose which targets to attack – as they already can – how should they be programmed to make that selection?

Computer scientists are teaming up with philosophers, psychologists, linguists, lawyers, theologians and human rights experts to identify the set of decision points that robots would need to work through in order to emulate our own thinking about right and wrong.

The NYT article even discusses the concept of programming “guilt” into robots, to help it learn to adapt

Just a quick post to:

1. Mark the new year;

2. Shout out to a nice shout out for structured journalism;

3. Take a moment to reflect on the strides we took in 2014.

The shout out comes via Josh Stearns, who pulled together a nice piece about the main trends he saw in 2014, including, of course, the development of structured journalism.   As he notes, the emphasis seems now to have shifted from new forms of presentation – think Snowfall – to more fundamentally different, data- and digitally-driven types of journalism (think Politifact, Homicide Watch and Connected China).

From sensors to structured journalism, crowdsourcing to podcasting, new modes of journalism that have been emerging over the last decade took huge strides forward this year. Communities of practice grew up around new models of storytelling to formalize norms, grapple with ethical and technical questions and tackle issues of sustainability.

And it’s true: The panel on structured journalism at ONA in Chicago last year – featuring Bill Adair, Laura Amico, Miguel Paz and yours truly – was a huge step forward in terms of putting the core ideas out in public, building a community around them, and as Laura noted during the talk, just coming up with a common vocabulary that facilitates discussion about how to move the concept forward.  Just the fact that Josh’s post uses the term is a reflection of that success. But it’s more than that.  There is a much bigger group of people who are thinking about other implementations of structured journalism, and there certainly seems to be a lot more excitement about the idea.  In his post, Josh cites a host of examples, from the use of card stacks in Vox to Emergent to Ballot Watch to The N-Word that are trying new ways to practice journalism, based on underlying data structures – not quite data journalism, and not exactly data visualizations, but sites that use data structure as a core building block of their journalism.  As he notes:

One of the most interesting sessions at the Online News Association conference this year was “From Data to Audience: Structured Journalism and Modern Narratives” in which the panelists explored how the fundamental elements of stories can be collected, organized, presented and reused (by journalists or members of the community) through unique story-driven databases. …  This is a form truly born of the web, and the early results are very exciting.

None of this would have been possible without the efforts – and initiative – of Bill, Laura and Chris Amico, who pushed for that, and other panels over the course of 2014.  Bill wants us to get together again early this year, and I’m looking forward to what further progress we can make this year.

Alas, Homicide Watch DC will soon be no more.

As the Washington Post notes – and founders Laura and Chris Amico previously announced – the site will be closing in a few days, at the end of the year, because of a lack of funding lack of a local partner. But arguably it’s really the victim of a lack of imagination.

Not on the part of Laura or Chris, of course; if anything, their problem was being too far ahead of the pack.  The lack of imagination is more about how we as an industry view what we do and why we do it.

Homicide Watch didn’t need a lot of money to keep going.  Laura figures operation costs are about $60,000 a year, the price of a single reporter. But they couldn’t come to terms with any local news organization, including the Post, to partner with them or acquire it. And so one of the signature examples of structured journalism won’t be around any more – although sister sites will continue to run.

At one level, it’s easy to understand why a site like this doesn’t fit cleanly into a mainstream news organization’s structure. Newspapers like the Post are focused on devoting their (limited) resources to stories with the biggest bang, and with the widest audience and arguably in the greatest public interest. Covering every murder, as Homicide Watch does, upturns that core news judgment in many ways, as I’ve written in the past.

As the Post noted in its piece:

Photographer Lloyd Wolf, known for his images of street memorials for murder victims, said Homicide Watch, at homicidewatch.org, called attention to cases that would “never clear the bar for news.”

He compared the more than 400 cases Homicide Watch has followed with the wall-to-wall coverage of the 2011 murder of an employee at a Lululemon Athletica clothing store in Bethesda. “Frankly, every piece of attention that was paid to that case was deserved and meaningful, but it happened because it was in a fancy neighborhood,” Wolf said. “There are thousands of other victims whom people are remembering to no public acclaim and to no public help.”

But why isn’t covering every murder – not just the more “newsworthy” ones – a core part of any local news organization’s mission, and especially when the costs of doing so aren’t particularly high,

Who controls traffic to your site – and how much does it really matter?

The answer to the first question is Facebook, at least according to a recent piece in the New York Times about the platform’s News Feed algorithm and its outsized role in determining how many people come to journalism these days.

The answer to the second question is – well, it depends.

It depends, in part, on whether we’re talking about journalism in general, the public interest, or the financial well-being of your site.  All of which yield potentially very different answers.

But first, the first question.  As the NYT piece notes:

…Facebook is at the forefront of a fundamental change in how people consume journalism. Most readers now come to it not through the print editions of newspapers and magazines or their home pages online, but through social media and search engines driven by an algorithm, a mathematical formula that predicts what users might want to read.

Certainly fewer and fewer people are coming to news sites via the home page; most are referred in through social media and search. And that raises all sorts of questions about how well they’re being informed about things that should matter to them, and, for that matter, about the kind of journalism that’s being created to cater to them.

The Times piece flags the fear that, increasingly, people are being being trapped in a “filter bubble” of only like-minded views, as they both select news that fits their preconceptions and search and filter algorithms become tuned to the sorts of things they’re likely to want to read.

The shift raises questions about the ability of computers to curate news, a role traditionally played by editors. It also has broader implications for the way people consume information, and thus how they see the world.

Fair enough.  And there’s no question news consumption habits are being re-invented at a dizzying pace.  But it’s not clear that machine curation of news is that much worse than human curation; it’s different, certainly, but just as machines do some things more poorly than humans, they also do some things better than humans.  A person looking for in-depth LGBT news in the mainstream media 20 years ago wouldn’t have been well-served by human editors then, and while that reader today may well get a better collection of such stories from a human editor, there probably aren’t enough editors out there to create great packages for every conceivable set of interests. Is having a machine curate a so-so set of stories for my interests that much worse than having a human curate a set of stories that I’m only marginally interested in?

True, there’s also the notion that there are stories all people should be exposed to, and have an interest in – but ask any marginalized group in any society whether that set of stories responds to their needs, and it’s not always clear that it does.

Just a quick post to say there was a nice turnout at our session at ONA, with a great panel of Laura Amico, Bill Adair and Miguel Paz (with yours truly moderating after a short intro/powerpoint, which I’ll post if I can ever figure out how to do that.)

Chris Amico did much to live-tweet the discussion – as did many others – and you can see much of that here.

So how did it go? Pretty well, at least from our point of view.  Laura did great work to ensure a community of people already involved in doing various forms of structured journalism were in the room, among them David Cohn of Circa and Melissa Bell of Vox.  We heard from others working on projects, such as David Caswell of Structured Stories, who’s building an interesting platform/CMS for structuring narrative.  And many more.

Having all those people in a single room meant we could share experiences, talk about the ideas and obstacles, and generally riff on where we thought all this might go. As Laura noted (and Chris tweeted):

While the concept seems really simple, we’ve really lacked the words to talk about this.

So just convening everyone, and having a common vocabulary was a big step forward. And then there were good points that were raised – from the cultural challenges of getting reporters to focus less on the stories they’re crafting and more on the information they’re collecting, to the future problems of maintaining ever-growing databases, to the possibilities inherent in having better newsroom calendars that we could build structure off.

Another point that stuck to me: How committing to collect some types of information regularly can upturn some of the notions of mission and news judgment in newsrooms. News organizations typically focus on outliers

It’s hard to make predictions, especially about the future – attributed to various people

How fast is the world changing – and how fast are we adapting to it?

Executive Summary:  Faster than we think, and not fast enough.  At least those are my takeaways from The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson and Andrew McAfee. an interesting book that’s simultaneously uplifting, optimistic, terrifying and worrying.  And definitely worth a read.

I’m not sure that’s what Messrs Brynjolfsson and McAfee intended as the main takeways – and there’s certainly plenty else in there about the economics (and inequalities) of the machine age, the spread of robotics, data and algorithms into our lives, the likely policy implications, and all that sort of thing – but at least that’s how I read it.

Not that it’s necessarily bad news – in fact, one of the best frames they put on the new digital age that we’re in is around the notion that, just as the industrial age freed us from the constraints of animal and human muscle power, this revolution is set to augment the human brain in ways we can’t yet really understand.

As they point out, a whole slew of new capabilities have emerged in the last few years that were effectively dismissed as essentially impossible in the near term less than a decade ago – self-driving cars, effective voice recognition, human-like text generation, visual recognition systems, and so on.  Even machine translation services, which are in many ways laughably bad, are a minor miracle in that they even exist.

What’s driving all this?  Increasingly powerful computing, decreasingly expensive sensors and processors that are proliferating in devices and objects, and the masses of data they’re generating every minute.  All that data isn’t necessarily useful – and certainly will be misused at some point – but it opens up lots of potential new capabilities.  But first we have to imagine them.

Look at Waze, for example, which took the ubiquitous location – and hence speed – data available on phones to build real-time traffic maps.  Before that, we had various mapping apps, that while useful, basically took what was static information and put them on mobile devices and added search and other capabilities.  Very useful, but much more an extension of existing systems.  It took the folks at Waze to reimagine what they could do with completely unrelated data.  So what other tricks are we missing?  (What if we used cellphone location data to help the blind navigate around other people in crowds?)

And, of course, I’m still plugging structured journalism.

Frederic Filloux, in a recent Monday Note, pointed to a host of potential news applications that could be built off the data in our mobile phones, going beyond simply the notions of shorter stories/small screen-friendly articles/quick updates that dominates a lot of the discussion of mobile apps.