Making an algorithmic economy work

Making an algorithmic economy workCreating its successes, and fixing its errors

Juan Mateos-Garcia6th December 2017

Surviving a new techno-economic paradigm

Sources: Wikipedia, Google n-grams

● What are its economic drivers?

● What’s its workforce?

● What are the opportunities and challenges?

In an information rich society, attention becomes the scarce resource

"...in an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes

the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently

among the overabundance of information sources that might consume it" (Simon 1971, pp. 40–41)

Sources: CMU, New York Times

Algorithms are a technology to manage excess information

Sources: Edureka

Systems that learn from examplesTransform an

information input into a prediction (and an action?)

Some economic characteristics of algorithms

Sources: Facebook, Google

Transferrable

The good

Scalable

The not-so-good

Fallible

Gamable

Implications for the workforce

Sources: Autor, Levy and Murnaane

Cognitive content

Routine Non-routine

Social (physical) content

Routine Assembly line Copy editor

Non routine Hairdresser Scientist

What characteristics of jobs complement / compete with automation?

Workers who create and use algorithmsWorkers who supplement and supervise algorithms

Function: Develop and apply algorithmsHigh creativity, high productivityIs the education system prepared to prepare this group?

Workers who create algorithms: ‘The sexiest occupation in the world’?

Sources: Nesta / RSS / UUK (2014)

Organisational implications

Sources: Nesta (2015)

% improvement in productivity for firms with higher than average levels in a variable (with all controls). All statistically significant.

Big implications for the organisation of the workplace as well

Move fast and break things?

Sources: XKCD

Increasing evidence of algorithmic error and gaming in the financial sector, media and society...

Workers who clean up after the algorithms: The worst occupation in the internet?

Sources: The Guardian, Nesta (2017)

Function: Detecting and fixing algorithmic errors and situations where the system is being gamedLow creativity, low productivity

Organisational implications

Sources: Google, YouTube, James Bridle

Supervision makes sense in high stakes domains. Also makes

algorithmic decision-making less scalable

Outsourcing supervision to users makes it cheaper but also has

costs

Human supervision as an early warning sign against algorithmic

failure

Conclusions and challenges

We need algorithms to operate in an information rich world, but they are bringing with them new divides between:● Superstars and supervisors● Objects and subjects○ Individual / community level○ Company level

Our ability to manage these tensions will determine if we harness these technologies for good or end in a dystopian scenario. It’s still up to us!

Questions● How does your organisation use

algorithms to manage the information overload?

● Are you making the most of their scalability and transferability?

● What are your defenses against algorithmic error?

nesta.org.uk

@nesta_uk

[email protected]@JMateosGarciahttps://www.linkedin.com/in/juanmateosgarcia