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D4p complex economics_ai_v2

Slides for paper about the Complex Economics of Artificial Intelligence presented at the Data for Policy Conference, London 11 June 2019.

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D4p complex economics_ai_v2

  1. 1. nesta.org.uk @nesta_uk The Complex Economics of Artificial intelligence Juan Mateos-Garcia [@JMateosGarcia] Data for Policy Conference London, 11th June 2019
  2. 2. Introduction Dimensions of complexity Implications General Purpose Technologies General Purpose Technologies transform the fabric of society
  3. 3. Introduction Dimensions of complexity Implications General Purpose Technologies [II] We should study its deployment with economics of complexity, not simplicity Artificial Intelligence is a hyper-General Purpose Technology
  4. 4. Introduction Dimensions of complexity Implications A definition Sensor EffectorAnalyzerPrediction Decision EnvironmentData Action • Model design and training • Data collection and processing • Reward function and decision system design • Supervision • Production system Skills ProcessesInfrastructure Data Functions Capabilities Inputs AI system Systems that respond flexibly to varied situations in order to inform or automate decisions: based on machine (deep) learning Fallible: Narrow, exploitable, unexplainable
  5. 5. Introduction Dimensions of complexity Implications Ways to think about it A powerful technology A dangerous technology Mass automation / labour displacement Discrimination and bias Market power Safety SuperintelligenceManipulation and exploitation Economics of AI AI is a technology that increases productivity It has economies of scale Importance of complementary assets Critical studies of AI AI is a socially constructed technology It reproduces injustice Importance of ethics, inclusion and regulation AI safety AI is a potentially unsafe technology It can be exploited and gamed, and it can exploit and game Importance of design, alignment and control
  6. 6. Introduction Dimensions of complexity Implications Towards a complex economics of AI Simple economics of AI Complex economics of AI Definition of technology An investment ($$) A system, a trajectory and a set of institutions Drivers of development Expected benefits and costs History, culture and politics Measure of impact Increased productivity, income share Externalities, diversity and flexibility, risk Role of time Lags due to the need to invest on complementary investments Sequences of events determine outcomes, random events matter Accepts the possibility of deployment failures (like critical studies and safety approaches) Acknowledges the importance of incentives and trade offs
  7. 7. Introduction Dimensions of complexity Implications The overall picture Organizational Complexity Market complexity Social complexity Temporal complexity a b c The interaction of components at lower levels generate structures at higher levels, and those structure shape behaviours at the lower levels Arthur (2014), Nelson and Winter (1982), Simon (1968)
  8. 8. Introduction Dimensions of complexity Implications Organisational complexity Organizational Complexity Uncertainty about adoption Deviation from desirable scenario Uneven, disappointing deployment Undesirable outcome a b c Organisations need to adopt fallible technologies and adapt their processes Integrating AI systems into complex organisational systems is easier for young digital sectors, and this could create an AI divide. Agrawal et al (2018) Bresnahan & Tratjenberg (1995)
  9. 9. Introduction Dimensions of complexity Implications Market complexity Market complexity Information asymmetries between actors Deviation from desirable scenario Mediocre, unsafe and abusive applications, internalization. Undesirable outcome Information asymmetries are pervasive in the market Visibility and malleability create ‘information thickets’ in AI markets Hadfield and Hadfield (2018), Lipton and Steinhardt (2018), Stilgoe et al (2013), Brundage et al (2018)
  10. 10. Introduction Dimensions of complexity Implications Sociopolitical complexity Social complexity Dilemmas between groups Deviation from desirable scenario Disruptive and unjust deployment Undesirable outcome AI deployment generates externalities ‘elsewhere in society’ Vulnerable groups are unfairly exposed to algorithmic risks Acemoglu & Restrepo (2019), Myers West et al (2019), Eubanks (2018)
  11. 11. Introduction Dimensions of complexity Implications Temporal complexity Temporal complexity Path dependency in time Deviation from desirable scenario Irreversibilities and lock-in to inferior trajectories Undesirable outcome Technological, market and social trajectories are cumulative and path dependent Some trajectories (deep learning?) get locked in because of their short term advantages despite their long term risks Marcus (2018), Arthur (1994), Dosi (1982)
  12. 12. Introduction Dimensions of complexity Implications Policy Intervention principle Evidence and experimentation Transparency and compliance Social solidarity Preservation of diversity Directionality in AI R&D&I Organizational Complexity Market complexity Social complexity Temporal complexity All the above create an active space for government intervention to shape AI trajectories of deployment and diffusion, and avoid undesirable outcomes These policies need to be informed by an evidence based developed through a new ‘Sciences (and policies) of the Artificial’. I believe that a complex view of AI economics will be a critical component of those new sciences.
  13. 13. nesta.org.uk @nesta_uk juan.mateos-garcia@nesta.org.uk @JMateosGarcia Essay: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3294552 Summary blog: https://www.nesta.org.uk/blog/complex-economics-artificial-intelligence/