[D66] To decarbonize we must decomputerize: why we need a Luddite revolution

[R.O.]

(Zelfs de Guardian omarmt de luddieten)


  To decarbonize we must decomputerize: why we need a Luddite revolution

Wed 18 Sep 2019 06.30 BST

By
Ben Tarnoff
theguardian.com
7 min
View Original 
<https://getpocket.com/redirect?url=https%3A%2F%2Fwww.theguardian.com%2Ftechnology%2F2019%2Fsep%2F17%2Ftech-climate-change-luddites-data>

The data server hall at Facebook’s storage center near in Lulea, Sweden. 
Data centers currently consume 200 terawatt hours per year – roughly the 
same amount as South Africa. Photograph: Simon Dawson/Bloomberg via 
Getty Images The data server hall at Facebook’s storage center near in 
Lulea, Sweden. Data centers currently consume 200 terawatt hours per 
year – roughly the same amount as South Africa. Photograph: Simon 
Dawson/Bloomberg via Getty Images

Our built environment is becoming one big computer. “Smartness” is 
coming to saturate our stores, workplaces, homes, cities. As we go about 
our daily lives, data is made, stored, analyzed and used to make 
algorithmic inferences about us that in turn structure our experience of 
the world. Computation encircles us as a layer, dense and 
interconnected. If our parents and our grandparents lived /with/ 
computers, we live /inside/ them.

A growing chorus of activists, journalists and scholars are calling 
attention to the dangers of digital enclosure. Employers are using 
<https://www.wsj.com/articles/the-new-ways-your-boss-is-spying-on-you-11563528604> 
algorithmic tools to surveil and control workers. Cops are using 
<https://stoplapdspying.org/resources/architecture/> algorithmic tools 
to surveil and control communities of color. And there is no shortage of 
dystopian possibilities on the horizon: landlords evicting tenants with 
“smart locks”, health insurers charging higher premiums because your 
Fitbit says you don’t exercise enough.

Digitization doesn’t just pose a risk to people, however. It also poses 
a risk to the planet. July was the hottest month on record. Large chunks 
of the Arctic are melting. In India, more than half a billion people 
face water shortages. Putting computation everywhere directly 
contributes to this crisis. Digitization is a climate disaster: if 
corporations and governments succeed in making vastly more of our world 
into data, there will be less of a world left for us to live in.

To understand the relationship between data and climate, the best place 
to start is machine learning (ML). Billions of dollars are being spent 
on researching, developing, and deploying ML because major breakthroughs 
in the past decade have made it a powerful tool for pattern recognition, 
whether analyzing faces or predicting consumer preferences. ML “learns” 
by training on large quantities of data. Computers are stupid: babies 
know what a face is within the first few months of being alive. For a 
computer to know what a face is, it must learn by looking at millions of 
pictures of faces.

This is a demanding process. It takes place inside the data centers we 
call the cloud, and much of the electricity that powers the cloud is 
generated by burning fossil fuels. As a result, ML has a large carbon 
footprint. In a recent paper <https://arxiv.org/pdf/1906.02243.pdf> that 
made waves in the ML community, a team at the University of 
Massachusetts, Amherst, found that training a model for natural-language 
processing – the field that helps “virtual assistants” like Alexa 
understand what you’re saying – can emit as much as 626,155lb of carbon 
dioxide. That’s about the same amount produced 
<https://www.theguardian.com/environment/ng-interactive/2019/jul/19/carbon-calculator-how-taking-one-flight-emits-as-much-as-many-people-do-in-a-year> 
by flying roundtrip between New York and Beijing 125 times.

Training models isn’t the only way ML contributes to the cooking of our 
planet. It has also stimulated a hunger for data that is probably the 
single biggest driver of the digitization of everything. Corporations 
and governments now have an incentive to acquire as much data as 
possible, because that data, with the help of ML, might yield valuable 
patterns. It might tell them who to fire, who to arrest, when to perform 
maintenance on a machine or how to promote a new product.

‘Digitization doesn’t just pose a risk to people. It also poses a risk 
to the planet. In India, more than half a billion people face water 
shortages.’ Photograph: R Parthibhan/AP ‘Digitization doesn’t just pose 
a risk to people. It also poses a risk to the planet. In India, more 
than half a billion people face water shortages.’ Photograph: R 
Parthibhan/AP

One of the best ways to make more data is to put small connected 
computers everywhere: Cisco predicts there will be 28.5bn networked 
devices by 2022. Aside from the energy required to manufacture and 
maintain those devices, the data they produce will live in the 
carbon-intensive cloud. Data centers currently consume 200 terawatt 
hours per year – roughly the same amount as South Africa. Anders Andrae, 
a widely cited researcher at Huawei, tells me that number is likely to 
grow 4-5 times by 2030. This would put the cloud on par with Japan, the 
fourth-biggest energy consumer on the planet.

What can be done to curb the carbon costs of data? Greenpeace has long 
pushed cloud providers to switch to renewable energy sources and improve 
efficiency. These efforts have seen some success: the use of renewables 
by data centers has grown 
<http://www.clickclean.org/downloads/ClickClean2016%20HiRes.pdf> 
substantially. Meanwhile, efficiency gains from better techniques and 
bigger economies of scale have moderated 
<https://iopscience.iop.org/article/10.1088/1748-9326/aaec9c> the 
cloud’s power consumption in recent years. When it comes to ML, a group 
of researchers are calling <https://arxiv.org/pdf/1907.10597.pdf> for a 
more energy-conscious approach, which they call “Green AI”. These are 
encouraging trends, and tech workers themselves are likely to play a key 
role in advancing them: Amazon employees have been organizing 
<https://www.theguardian.com/technology/2019/may/22/amazon-workers-climate-crisis-board-jeff-bezos> 
for a climate plan since late last year, and they recently announced 
<https://medium.com/@amazonemployeesclimatejustice/amazon-employees-are-joining-the-global-climate-walkout-9-20-9bfa4cbb1ce3> 
a global walkout for 20 September. Among their demands is for the 
company to commit to zero emissions by 2030 and to stop selling cloud 
services to fossil fuel companies.

But it’s clear that confronting the climate crisis will require 
something more radical than just making data greener. That’s why we 
should put another tactic on the table: making less data. We should 
reject the assumption that our built environment /must/ become one big 
computer. We should erect barriers against the spread of “smartness” 
into all of the spaces of our lives.

To decarbonize, we need to decomputerize.

This proposal will no doubt be met with charges of Luddism. Good: 
Luddism is a label to embrace. The Luddites were heroic figures and 
acute technological thinkers. They smashed textile machinery in 
19th-century England because they had the capacity to perceive 
technology “in the present tense”, in the words 
<https://books.google.com/books?id=csndAGqJlk0C> of the historian David 
F Noble. They didn’t wait patiently for the glorious future promised by 
the gospel of progress. They saw what certain machines were doing to 
them in the present tense – endangering their livelihoods – and 
dismantled them.

We are often sold a similar bill of goods: big tech companies talk 
incessantly about how “AI” and digitization will bring a better future. 
In the present tense, however, putting computers everywhere is bad for 
most people. It enables advertisers, employers and cops to exercise more 
control over us – in addition to helping heat the planet.

Fortunately, there are latter-day Luddites working to stem the tide. 
Community groups like the Stop LAPD Spying Coalition 
<https://stoplapdspying.org/> are organizing to shut down algorithmic 
policing programs. A growing campaign to ban the government use of 
facial recognition software has won important victories in San Francisco 
<https://www.nytimes.com/2019/05/14/us/facial-recognition-ban-san-francisco.html> 
and Somerville 
<https://www.bostonglobe.com/metro/2019/06/27/somerville-city-council-passes-facial-recognition-ban/SfaqQ7mG3DGulXonBHSCYK/story.html>, 
Massachusetts, while workers at Amazon are calling 
<https://gizmodo.com/amazon-workers-demand-jeff-bezos-cancel-face-recognitio-1827037509> 
for the company to stop selling such software to law enforcement. And in 
the streets of Hong Kong, protesters are developing techniques for 
evading the algorithmic gaze, using lasers to confuse facial recognition 
cameras and cutting 
<https://twitter.com/Jordan_Sather_/status/1165327628825284610> down 
“smart” lamp-posts equipped with monitoring devices.

Teenagers and students take part in a climate protest outside the White 
House in Washington on 13 September 2019. Photograph: Nicholas 
Kamm/AFP/Getty Images Teenagers and students take part in a climate 
protest outside the White House in Washington on 13 September 2019. 
Photograph: Nicholas Kamm/AFP/Getty Images

These are just a few possible sources of inspiration for a broader 
movement for decomputerization, one that pursues social and ecological 
goals simultaneously. The premise of the Green New Deal is that we can 
make society greener and more equitable at the same time – that we can 
democratize as we decarbonize. We should apply the same logic to our 
digital sphere. Preventing a local police department from constructing 
an ML-powered panopticon is a matter of algorithmic, social /and/ 
climate justice. As they used to say in the 1960s: one struggle, many 
fronts.

For such a struggle to be successful, however, resistance is not enough. 
We also need a vision of the future we want. Again, the history of the 
Luddites can be helpful. In 1812, a group of Yorkshire Luddites sent a 
factory owner a letter promising continued action until “the House of 
Commons passes an Act to put down all Machinery hurtful to Commonality”. 
Following their example, we might derive a simple Luddite principle for 
democratizing technology: we should destroy machinery hurtful to the 
common good and build machinery helpful to it.

What does this mean in practice? It’s hard to think of anything more 
hurtful to our common life than heating large portions of the planet 
beyond habitable levels. Privacy advocates have long called for 
companies to restrict their collection of data to the minimum necessary 
to perform a service – a principle now enshrined in the GDPR, the EU’s 
omnibus data regulation. A 21st-century Luddism should embrace this 
principle but go further. What matters is not only how much data a 
service collects, but what imprint that service leaves upon the world – 
and thus whether it should be performed at all.

Decomputerization doesn’t mean /no/ computers. It means that not /all/ 
spheres of life should be rendered into data and computed upon. 
Ubiquitous “smartness” largely serves to enrich and empower the few at 
the expense of the many, while inflicting ecological harm that will 
threaten the survival and flourishing of billions of people.

Precisely which computational activities should be preserved in a less 
computerized world is a matter for those billions of people themselves 
to decide. The question of whether a particular machine hurts or helps 
the common good can only be answered by the commons itself. It can only 
be answered collectively, through the experiment and argument of democracy.

The zero-carbon commonwealth of the future must empower people to decide 
not just /how/ technologies are built and implemented, but /whether/ 
they’re built and implemented. Progress is an abstraction that has done 
a lot of damage over the centuries. Luddism urges us to consider: 
progress towards what and progress for whom? Sometimes a technology 
shouldn’t exist. Sometimes the best thing to do with a machine is to 
break it.


-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://www.tuxtown.net/pipermail/d66/attachments/20200730/a8eef2f9/attachment-0001.html>