making an algorithmic economy work
TRANSCRIPT
![Page 1: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/1.jpg)
Making an algorithmic economy workCreating its successes, and fixing its errors
Juan Mateos-Garcia6th December 2017
![Page 2: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/2.jpg)
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?
![Page 3: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/3.jpg)
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
![Page 4: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/4.jpg)
Algorithms are a technology to manage excess information
Sources: Edureka
Systems that learn from examplesTransform an
information input into a prediction (and an action?)
![Page 5: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/5.jpg)
Some economic characteristics of algorithms
Sources: Facebook, Google
Transferrable
The good
Scalable
The not-so-good
Fallible
Gamable
![Page 6: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/6.jpg)
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
![Page 7: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/7.jpg)
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)
![Page 8: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/8.jpg)
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
![Page 9: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/9.jpg)
Move fast and break things?
Sources: XKCD
Increasing evidence of algorithmic error and gaming in the financial sector, media and society...
![Page 10: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/10.jpg)
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
![Page 11: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/11.jpg)
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
![Page 12: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/12.jpg)
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!
![Page 13: Making an algorithmic economy work](https://reader031.vdocuments.us/reader031/viewer/2022030318/5a65cbd07f8b9ad05e8b4695/html5/thumbnails/13.jpg)
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?