Making an algorithmic economy work
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This presentation outlines the drivers of an algorithmic economy, the people who work on it and its challenges. I pay special attention to the risks of algorithmic error and safety, and the human supervisors in charge of managing it.
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Making an algorithmic economy work
- 1. Making an algorithmic economy work Creating its successes, and fixing its errors Juan Mateos-Garcia 6th December 2017
- 2. 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?
- 3. 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
- 4. Algorithms are a technology to manage excess information Sources: Edureka Systems that learn from examples Transform an information input into a prediction (and an action?)
- 5. Some economic characteristics of algorithms Sources: Facebook, Google Transferrable The good Scalable The not-so-good Fallible Gamable
- 6. 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 algorithms Workers who supplement and supervise algorithms
- 7. Function: Develop and apply algorithms High creativity, high productivity Is the education system prepared to prepare this group? Workers who create algorithms: ‘The sexiest occupation in the world’? Sources: Nesta / RSS / UUK (2014)
- 8. 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
- 9. Move fast and break things? Sources: XKCD Increasing evidence of algorithmic error and gaming in the financial sector, media and society...
- 10. 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 gamed Low creativity, low productivity
- 11. 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
- 12. 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!
- 13. 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?