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Fiction for real
Activity 5

Fiction for real

Text A
“Times Dystopian Novels Predicted Real Life Events” Laura Allan,, 2016.

Real people sometimes come home, hop into a chair, and then watch television shows all about the lives of other real, sometimes deplorable people. This is entertainment now. We just sit, watch other people’s lives, and ignore our own for a bit. Sound familiar? If you read Fahrenheit 451, you sure bet it does! You see, in this novel, everyone watches something called “The family”. Basically, several walls of your home are screens that show the family and their trivial little problems [...]. Bradbury predicted this kind of escapism TV back in 1953, before any of the current Kardashians were even born (that lucky man).

“Times Dystopian Novels Predicted Real Life Events” Laura Allan,, 2016.
Text B
“An Episode From Netflix’s ‘Black Mirror’ Uses Creepy Technology That May Become a Reality in China”, Lenyon Whitaker, Metro USA, 2018.

Although the Black Mirror Nosedive episode is purely fiction, the way it showed how some people become obsessed with personal ratings in everyday life may have predicted the future in China and their social credit system. People may soon become more obsessed with their credit scores when there are other factors involved that go beyond paying bills on time.

“An Episode From Netflix’s ‘Black Mirror’ Uses Creepy Technology That May Become a Reality in China”, Lenyon Whitaker, Metro USA, 2018.
Text C
“Can you police by machine?”,, 2019.

Liberty has warned that the growing use of predictive crime mapping by British police forces poses a threat to citizens’ rights and freedoms. [...]

Among the warnings set out in the report are concern that police algorithms: entrench pre-existing discrimination; increase the risk of “automation bias”; and lack transparency. [...]

Last year, Kent Police quietly scrapped a five-year pilot project that trialled computer software designed to predict and prevent crime1. Technology developed by US firm Predpol involved machine learning algorithms that were able to predict when and where a crime might take place by processing data from records on previous criminal activity. While it has been used by the Los Angeles Police Department since 2011, it was the first time the technology was deployed in England and Wales.

It “had a good record of predicting where crimes are likely to take place”, John Phillips, a superintendent at Kent Police, told the Financial Times. But he added: “What is more challenging is to show that we have been able to reduce crime with that information.”

“Can you police by machine?”,, 2019.
1. as in Minority Report, by Steven Spielberg, 2002
Voir les réponses

You are in charge of one text.

Workbook p. 89

Be ready to briefly present it.
a. Identify the technological danger evoked.

b. How could it affect the world we live in?

Useful vocabulary:
The text exposes…
This is an excerpt / extract from...
When the author mentions…, he / she means...

Let's talk this out!

Voir les réponses

Share your findings with your classmates and learn about the other texts.

What are the common points and differences between your texts?

Which is the scariest technological danger? Discuss.

Useful vocabulary:
You cannot ignore…
This is by far the scariest because…
The danger that scares me most is…
I’m adamant about… I have no doubt…
It will have drastic consequences if…
What could be worse than… is...

Over to you!

I have a rebuttal!

Let’s use what you have learnt in and / or !

As a Harvard student taking the Emerging Technologies and Ethics class, you participate in a debate on the benefits and drawbacks of new technologies. Prepare five arguments in favour and five against them. Oppose your classmates’ arguments online!.

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