20200
P
1
20100
R  E
2
2070
D   I   C
3
2050
T   I   O   N
4
2040
S   F   O   R   J
5
2030
O  U  R  N  A  L
6
2020
I  S  M  2  0  2  0
7

AI can’t conjure up an Errol Morris

“AI is set out to kill most of the remaining newsroom jobs. But it can never replace how Errol Morris, Bob Woodward, or Michael Barbaro construct long-form affective narratives — which is the future of journalism.”

Artificial intelligence is to the 2020s what computers were to the 1980s. It’s more of a new trend than a new paradigm — and it will take a decade (and billions of dollars spent on events, commissions, and reports) before people tangibly grasp what it means.

What’s usually missing from the current debate is that AI, despite its many potential applications for the public good, is primarily used for two things: prediction and profit. Or prediction-based profit.

At the heart of AI lies big data, and at the core of data is its unprecedented predictive powers. It’s this power that has incited the ever more invisible but aggressive global conquest of data to extract and collect. This is what Nick Couldry and Ulises A. Mejias call “data colonialism” in their recent book The Costs of Connection: How Data is Colonizing Human Life and Appropriating it for Capitalism. “Just as historical colonialism paved the way for industrial capitalism,” they write, “data colonialism is paving the way for a new stage of capitalism whose outlines we only partly see: the capitalization of life without limit.”

When it comes to journalism, the same logic follows. AI technologies are now capable of doing almost every aspect of the news practice with a fraction of the costs in the long term. A recent report by the London School of Economics examined how 71 news organizations around the world are working with AI in three areas:

  • Newsgathering: sourcing of information, story idea generation, identifying trends, investigations, event or issue monitoring, extracting information or content.
  • News production: content creation, editing, packaging for different formats and platforms, text, image and video creation, repurposing content for different audiences.
  • News distribution: personalization, marketing, finding audiences, understanding user behaviour, monetization/subscriptions.

The underlying message of these kinds of reports is that the AI capture of newsrooms will kill more jobs than we can imagine. It will also kill many newsrooms who can’t afford to invest in AI technologies to lower their costs in order to survive.

However, as I wrote here last year, news (short-form rational factual narratives), which for over two centuries was the main output of journalism, is losing to factual drama (long-form affective narratives) in its cultural relevance and thereby commodity value. Journalism is reinventing itself in a form which I call “post-news journalism.”

It’s true that AI technologies are being used to generate non-newsy journalism in areas such as travel, fashion, culture, and the arts. Media startups are working hard to take massive amounts of user-generated content and turn it into journalistic products to then monetize. But if AI can now generate news reports or travel reviews, it’s difficult to imagine it producing convincing factual drama. Emotions and affect are outside of the domain of reason, and predictive analytics and machine learning operate based on the logic of mathematics — even if they’re asked to generate emotions, they need rational methods to do so.

AI is set out to kill most of the remaining newsroom jobs. But it can never replace how Errol Morris, Bob Woodward, or Michael Barbaro construct long-form affective narratives — which is the future of journalism.

Hossein Derakhshan is a London-based media researcher and a former fellow at Harvard’s Shorenstein Center.

Artificial intelligence is to the 2020s what computers were to the 1980s. It’s more of a new trend than a new paradigm — and it will take a decade (and billions of dollars spent on events, commissions, and reports) before people tangibly grasp what it means.

What’s usually missing from the current debate is that AI, despite its many potential applications for the public good, is primarily used for two things: prediction and profit. Or prediction-based profit.

At the heart of AI lies big data, and at the core of data is its unprecedented predictive powers. It’s this power that has incited the ever more invisible but aggressive global conquest of data to extract and collect. This is what Nick Couldry and Ulises A. Mejias call “data colonialism” in their recent book The Costs of Connection: How Data is Colonizing Human Life and Appropriating it for Capitalism. “Just as historical colonialism paved the way for industrial capitalism,” they write, “data colonialism is paving the way for a new stage of capitalism whose outlines we only partly see: the capitalization of life without limit.”

When it comes to journalism, the same logic follows. AI technologies are now capable of doing almost every aspect of the news practice with a fraction of the costs in the long term. A recent report by the London School of Economics examined how 71 news organizations around the world are working with AI in three areas:

  • Newsgathering: sourcing of information, story idea generation, identifying trends, investigations, event or issue monitoring, extracting information or content.
  • News production: content creation, editing, packaging for different formats and platforms, text, image and video creation, repurposing content for different audiences.
  • News distribution: personalization, marketing, finding audiences, understanding user behaviour, monetization/subscriptions.

The underlying message of these kinds of reports is that the AI capture of newsrooms will kill more jobs than we can imagine. It will also kill many newsrooms who can’t afford to invest in AI technologies to lower their costs in order to survive.

However, as I wrote here last year, news (short-form rational factual narratives), which for over two centuries was the main output of journalism, is losing to factual drama (long-form affective narratives) in its cultural relevance and thereby commodity value. Journalism is reinventing itself in a form which I call “post-news journalism.”

It’s true that AI technologies are being used to generate non-newsy journalism in areas such as travel, fashion, culture, and the arts. Media startups are working hard to take massive amounts of user-generated content and turn it into journalistic products to then monetize. But if AI can now generate news reports or travel reviews, it’s difficult to imagine it producing convincing factual drama. Emotions and affect are outside of the domain of reason, and predictive analytics and machine learning operate based on the logic of mathematics — even if they’re asked to generate emotions, they need rational methods to do so.

AI is set out to kill most of the remaining newsroom jobs. But it can never replace how Errol Morris, Bob Woodward, or Michael Barbaro construct long-form affective narratives — which is the future of journalism.

Hossein Derakhshan is a London-based media researcher and a former fellow at Harvard’s Shorenstein Center.

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