Toward better tech journalism

“Could technology journalists be in service of something better than more speech, improved content moderation, unbiased algorithms, and consensual surveillance capitalism?”

Continuing Nieman Lab’s tradition of expressing hopes as predictions, I see an even better kind of technology journalism rising in 2021.

Reporting on how technology shapes and reflects social, political, economic, and cultural life has improved greatly in recent years. Coverage has moved far beyond the business, innovation, gadget, and trade beats, and fewer stories, thankfully, celebrate lone inventors, warn of robot workforces, ignore racist and sexist histories, or see only romanticized freedom in teleworking, “smart” homes, and digital assistants.

Relatively quickly, it’s become almost normal to see mainstream stories critical of machine learning, surveillance technologies, social media platforms, labor exploitation, and more. And these stories seem to be having real impact. From antitrust lawsuits and facial recognition bans to content moderation improvements and the beginnings of better algorithmic oversight, a powerful mix of scholars, activists, policymakers, funders, and journalists has taught us how to see and resist technological power.

But technology journalism could be better. Specifically, my hope for 2021 is that technology reporters ask themselves three questions:

  • Who are my usual sources, and where do they come from?
  • How well do I understand academic research on technology?
  • What vision of public life does my reporting assume?

First: Who are your usual sources for technology stories and where do they come from? Quote circuits definitely exist, and smart journalists on tight deadlines use them to get fast, predictable, and digestible information from people they trust. The shorthands and shared worldviews of strong reporter–source relationships make conversations faster and stories tighter. I’ve been that source, and I know some of those journalists.

But when technology journalists use social media to develop source relationships they run the risk of relying on networks that are too small, too predictable, and too reflective of the very technological power their reporting should view skeptically. Research tells us that journalists rely heavily on Twitter, that such reliance creates biases, and that journalists too quickly mistake what they see on social media for public opinion.

These dependencies, biases, and false equivalences not only exclude many women, trans, and BIPOC people, but that they also feed skewed images of social media fame among academics.

My experience tells me that the vast majority of professors are good-faith scholars eager to argue truths in much the same way good journalists do. But we are also increasingly tacitly (and sometimes explicitly) expected to show the public “relevance” and “impact” of our work. Appearances in news stories and journalists’ Twitter feeds can be powerful ways to make our work seen and cited, earn speaking and consulting engagements, win grants, and get promotions.

Journalists might take comfort in a professor’s social media fame as a kind of proxy for intellectual credibility, but audiences end up with a relatively small set of savvy sources informing stories, defining technologies, framing stakes.

You shouldn’t need to live on Twitter to be an authoritative academic source for a story about technology. I worry what message the social media quote circuit sends to graduate students and non-tenured academics struggling to meet increasingly unrealistic expectations of productivity and impact. And I worry about the richness of the technology reporting that results.

Pushing this further: How are your usual sources funded? What tactics have they used to get on your radar? What interests are they pursuing, which audiences do they want their quotes to reach, which stories would they prefer not be told? What definitions of technology, types of research, and social stakes are they pushing?

Are audiences really seeing sufficiently diverse definitions of technology, intellectual traditions, and theories of change? If you gushed over The Social Dilemma or the Facebook Oversight Board, then your source network probably has a particular shape. But if your Twitter networks skewered that documentary or celebrate the “real” oversight board, then you’re likely influenced by images of technology and theories of change with different, but still very strategic, aims.

To be clear, I am not bothsidesing technology debates or asking for false equivalency; some perspectives deserve no coverage. Rather, I’m hoping for technology journalism that sees its source networks critically, challenges academic fame-seeking, and rejects the assumption that “Twitter is real life.”

This brings me to a second question: How well do you understand academic research on technology?

Often, it seems, the research cited in technology reporting centers data and data science. Where is the data, who has it, is it “big,” is it new or old, is it anonymized, is it cross-platform, who paid for it, does it really say what people think it says?

These important questions can drive powerful scholarship and journalism, but they privilege one part of academia — a part that uses words like “cause,” “proof,” and “Science” with reverence and that often casually derides other evidence as “anecdotal” or “storytelling.”

There are other parts of academia with much to offer journalists trying to make sense of technology. We study how technology companies design systems, create policies, categorize people, make exceptions, define success, and gloss failure. We study how ideas and people dominate technological cultures and histories, which people and parts of society technologists see and ignore, which visions of the future have historically failed, and which ones seem perennially new. We don’t need server data for that.

We need people to talk with us, trust us, be vulnerable, tell us stories, and share folk theories. We need them to let us access archives, introduce us to their colleagues, help us decode marketing materials, tell us who they trust and who they fear, which parts of their education were valuable or irrelevant, and teach us how they understand the forces that create powerful technologies.

This is hard access to get, and these are tough relationships to build. And sometimes we’re hit with a double whammy of exclusion. Technology companies sometimes mistake us for journalists telling gotcha stories or activists with axes to grind; we get shuttled to pleasant but strategically unhelpful corporate communications staff. Then, if we’re lucky enough to talk with journalists about our technology research, as sources in their stories, we’re sometimes asked: “Okay, but do you have data on that?”

I and other interpretive, qualitative researchers have been asked some version of this question by well-meaning journalists. Sometimes our words become background context to set up the discussion of data science research, or we’re asked to interpret the findings of a “scientific study.” Or we’re put on the spot for grand solutions, asked what technology companies should do — to which I have sometimes replied, “Give me the access I need to be able to answer that question as robustly as I want to.”

My plea to technology journalists is this: Help us help you. See our data as real data, take up our access causes, and help us create cultures where it’s okay for people working within technology companies to talk to us without fear. Together, we could understand technologies far better that we do now, and in ways that are different from our data scientist colleagues.

Finally: Technology journalists, what image of public life drives your reporting and the technologies you cover?

This question asks journalists to be skeptical of the information-driven visions of public life that tend to drive both journalism and technology. Do your stories assume that more speech is better, that we just need to figure out how to filter out bad speech faster and at scale? Do they say that algorithms might be broken, but that more and better training data will solve the problem? Are they unsure of whether filter bubbles and echo chambers exist, but sure that simply opening and tweaking black-box recommendations systems will fix things? And do they think that too-big companies need to be broken up into smaller pieces, but also trust that competition will naturally follow and create a diverse marketplace of ideas?

These information-heavy frames dominate both technology companies and technology reporting, making it hard to see how technology journalism is both one of the most powerful and potentially broken parts of the press. Technology journalists need to ask themselves if their reporting is simply trying to make better information systems, creating tweaks at the margins of imagined public life, and calling for more accountable systems that they never really question.

Could technology journalists be in service of something better than more speech, improved content moderation, unbiased algorithms, and consensual surveillance capitalism?

Different journalists will answer these questions differently. There’s no one right vision of public life. But all journalists must know how their answers depend on their source networks, their relationships to academia, their understandings of technology, and their assumptions about public life.

To be sure, technology journalism is a big field. It is both untrue and unfair to assume that no reporter asks and answers these questions thoughtfully. Technology journalism has improved greatly in a short time, and I think we are in a golden era of accountability that is finally starting to question the unchecked hubris of technologists.

In the dumpster fire of 2020 — with pandemic viruses, racialized pain, economic inequalities, climate collapses, democratic crises, and journalistic layoffs — we can create a new kind of public life, in part, through better technology reporting. In her book on how to salvage and sustain life in the face of destruction and collapse, Anna Tsing says that we build powerful stories “through layered and disparate practices of knowing and being. If the components clash with each other, this only enlarges what such stories can do.”

I look forward to a 2021 of clashing components and powerful stories.

Mike Ananny is an associate professor at USC’s Annenberg School for Communication and Journalism.

Continuing Nieman Lab’s tradition of expressing hopes as predictions, I see an even better kind of technology journalism rising in 2021.

Reporting on how technology shapes and reflects social, political, economic, and cultural life has improved greatly in recent years. Coverage has moved far beyond the business, innovation, gadget, and trade beats, and fewer stories, thankfully, celebrate lone inventors, warn of robot workforces, ignore racist and sexist histories, or see only romanticized freedom in teleworking, “smart” homes, and digital assistants.

Relatively quickly, it’s become almost normal to see mainstream stories critical of machine learning, surveillance technologies, social media platforms, labor exploitation, and more. And these stories seem to be having real impact. From antitrust lawsuits and facial recognition bans to content moderation improvements and the beginnings of better algorithmic oversight, a powerful mix of scholars, activists, policymakers, funders, and journalists has taught us how to see and resist technological power.

But technology journalism could be better. Specifically, my hope for 2021 is that technology reporters ask themselves three questions:

  • Who are my usual sources, and where do they come from?
  • How well do I understand academic research on technology?
  • What vision of public life does my reporting assume?

First: Who are your usual sources for technology stories and where do they come from? Quote circuits definitely exist, and smart journalists on tight deadlines use them to get fast, predictable, and digestible information from people they trust. The shorthands and shared worldviews of strong reporter–source relationships make conversations faster and stories tighter. I’ve been that source, and I know some of those journalists.

But when technology journalists use social media to develop source relationships they run the risk of relying on networks that are too small, too predictable, and too reflective of the very technological power their reporting should view skeptically. Research tells us that journalists rely heavily on Twitter, that such reliance creates biases, and that journalists too quickly mistake what they see on social media for public opinion.

These dependencies, biases, and false equivalences not only exclude many women, trans, and BIPOC people, but that they also feed skewed images of social media fame among academics.

My experience tells me that the vast majority of professors are good-faith scholars eager to argue truths in much the same way good journalists do. But we are also increasingly tacitly (and sometimes explicitly) expected to show the public “relevance” and “impact” of our work. Appearances in news stories and journalists’ Twitter feeds can be powerful ways to make our work seen and cited, earn speaking and consulting engagements, win grants, and get promotions.

Journalists might take comfort in a professor’s social media fame as a kind of proxy for intellectual credibility, but audiences end up with a relatively small set of savvy sources informing stories, defining technologies, framing stakes.

You shouldn’t need to live on Twitter to be an authoritative academic source for a story about technology. I worry what message the social media quote circuit sends to graduate students and non-tenured academics struggling to meet increasingly unrealistic expectations of productivity and impact. And I worry about the richness of the technology reporting that results.

Pushing this further: How are your usual sources funded? What tactics have they used to get on your radar? What interests are they pursuing, which audiences do they want their quotes to reach, which stories would they prefer not be told? What definitions of technology, types of research, and social stakes are they pushing?

Are audiences really seeing sufficiently diverse definitions of technology, intellectual traditions, and theories of change? If you gushed over The Social Dilemma or the Facebook Oversight Board, then your source network probably has a particular shape. But if your Twitter networks skewered that documentary or celebrate the “real” oversight board, then you’re likely influenced by images of technology and theories of change with different, but still very strategic, aims.

To be clear, I am not bothsidesing technology debates or asking for false equivalency; some perspectives deserve no coverage. Rather, I’m hoping for technology journalism that sees its source networks critically, challenges academic fame-seeking, and rejects the assumption that “Twitter is real life.”

This brings me to a second question: How well do you understand academic research on technology?

Often, it seems, the research cited in technology reporting centers data and data science. Where is the data, who has it, is it “big,” is it new or old, is it anonymized, is it cross-platform, who paid for it, does it really say what people think it says?

These important questions can drive powerful scholarship and journalism, but they privilege one part of academia — a part that uses words like “cause,” “proof,” and “Science” with reverence and that often casually derides other evidence as “anecdotal” or “storytelling.”

There are other parts of academia with much to offer journalists trying to make sense of technology. We study how technology companies design systems, create policies, categorize people, make exceptions, define success, and gloss failure. We study how ideas and people dominate technological cultures and histories, which people and parts of society technologists see and ignore, which visions of the future have historically failed, and which ones seem perennially new. We don’t need server data for that.

We need people to talk with us, trust us, be vulnerable, tell us stories, and share folk theories. We need them to let us access archives, introduce us to their colleagues, help us decode marketing materials, tell us who they trust and who they fear, which parts of their education were valuable or irrelevant, and teach us how they understand the forces that create powerful technologies.

This is hard access to get, and these are tough relationships to build. And sometimes we’re hit with a double whammy of exclusion. Technology companies sometimes mistake us for journalists telling gotcha stories or activists with axes to grind; we get shuttled to pleasant but strategically unhelpful corporate communications staff. Then, if we’re lucky enough to talk with journalists about our technology research, as sources in their stories, we’re sometimes asked: “Okay, but do you have data on that?”

I and other interpretive, qualitative researchers have been asked some version of this question by well-meaning journalists. Sometimes our words become background context to set up the discussion of data science research, or we’re asked to interpret the findings of a “scientific study.” Or we’re put on the spot for grand solutions, asked what technology companies should do — to which I have sometimes replied, “Give me the access I need to be able to answer that question as robustly as I want to.”

My plea to technology journalists is this: Help us help you. See our data as real data, take up our access causes, and help us create cultures where it’s okay for people working within technology companies to talk to us without fear. Together, we could understand technologies far better that we do now, and in ways that are different from our data scientist colleagues.

Finally: Technology journalists, what image of public life drives your reporting and the technologies you cover?

This question asks journalists to be skeptical of the information-driven visions of public life that tend to drive both journalism and technology. Do your stories assume that more speech is better, that we just need to figure out how to filter out bad speech faster and at scale? Do they say that algorithms might be broken, but that more and better training data will solve the problem? Are they unsure of whether filter bubbles and echo chambers exist, but sure that simply opening and tweaking black-box recommendations systems will fix things? And do they think that too-big companies need to be broken up into smaller pieces, but also trust that competition will naturally follow and create a diverse marketplace of ideas?

These information-heavy frames dominate both technology companies and technology reporting, making it hard to see how technology journalism is both one of the most powerful and potentially broken parts of the press. Technology journalists need to ask themselves if their reporting is simply trying to make better information systems, creating tweaks at the margins of imagined public life, and calling for more accountable systems that they never really question.

Could technology journalists be in service of something better than more speech, improved content moderation, unbiased algorithms, and consensual surveillance capitalism?

Different journalists will answer these questions differently. There’s no one right vision of public life. But all journalists must know how their answers depend on their source networks, their relationships to academia, their understandings of technology, and their assumptions about public life.

To be sure, technology journalism is a big field. It is both untrue and unfair to assume that no reporter asks and answers these questions thoughtfully. Technology journalism has improved greatly in a short time, and I think we are in a golden era of accountability that is finally starting to question the unchecked hubris of technologists.

In the dumpster fire of 2020 — with pandemic viruses, racialized pain, economic inequalities, climate collapses, democratic crises, and journalistic layoffs — we can create a new kind of public life, in part, through better technology reporting. In her book on how to salvage and sustain life in the face of destruction and collapse, Anna Tsing says that we build powerful stories “through layered and disparate practices of knowing and being. If the components clash with each other, this only enlarges what such stories can do.”

I look forward to a 2021 of clashing components and powerful stories.

Mike Ananny is an associate professor at USC’s Annenberg School for Communication and Journalism.

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