DATA-DRIVEN INNOVATION

Transcription

1 Working Group on Legal Questions related to the Development of Robotics and Artificial Intelligence 26 May 2015 DATA-DRIVEN INNOVATION and the implications on jobs and skills

2 What is Data-Driven Innovation (DDI)? DDI refers to the use of data and analytics to improve or foster new products, processes, organisational methods and markets 2

3 Big data feeding ML algorithms to enable autonomous decision making Knowledge base Decision making Data value cycle Value added growth and well-being Data analytics (machine learning, ML) Datafication and data collection Big data 3

4 DDI enables next generation autonomous machines and systems Health Manufacturing Logistic Agriculture Transportation Finance 4

5 Example: algorithmic trading in finance Algorithmic trading as share of total trading Note: based on estimates. Source: OECD based on The Economist (2012) and Aite Group 5

6 IoT will be the next game changer Internet of Things (IoT) embedding physical objects in data flows (and intelligence) Driverless cars enabled by information flows from road infrastructure, other cars, and web services IoT empowering, but also embedding humans in data flows The IoT requires thinking about how humans and things cooperate differently when things get smarter. (Tim O Reilly) Leading to the emergence of an intelligent superorganism? 6

7 Towards the next production revolution? 7

8 What are the employment implications? 1. Who will be the losers and winners in the race against the machines? 2. Do we have the capacity to dance with the machines? 3. What are the challenges faced by the human dancer? 8

9 WHO WILL BE THE LOSERS AND WINNERS?

10 We have to learn from history! Handmade damasks Jacquard loom punch cards Mechanical tabulator 10

11 Capacities needed to successfully race against the machines Frey and Osborne (2013) Creative intelligence Social intelligence Complex perception and manipulation Levy and Murnane (2013) Solving unstructured problems Working with new information Non-routine manual tasks Elliott (2014) Language reasoning Vision movement 11

12 Solving unstructured problems and working with data will be key! Index of Changing Work Tasks in the U.S. Economy Source: Levy and Murnane,

13 Implications on inequalities Trends in wages for full-time, full-year male workers in the United States, Source: Brynjolfsson and McAfee, 2014 based on Acemoglu and Autor (2011) 13

14 with the share of income going to labour declining steadily Australia Germany Japan United States Source: OECD Unit Labour Costs Annual Indicators 14

15 What used to be attributed to labour is now knowledge-based capital. Labour Creativity Expert decision making Organisational know-how Capital IP Software (e.g. ERP, algorithms) Marketing, sales, customer relations Data ( Big ) Ownership of autonomous machines and systems will be defined by IP rights 15

16 DO WE HAVE THE CAPACITY TO DANCE WITH THE MACHINE?

17 Poor ICT adoption in many businesses! The diffusion of selected ICT tools and activities in enterprises, 2013 Percentage of enterprises with ten or more persons employed Highest Lowest 1st and 3rd quartiles Median Average % 100 FIN FIN GRC TUR NZL ISL SWE PRT FIN NZL KOR 20 0 TUR Broadband Website E-purchases Social network ERP Supply chain mngt. (ADE) CZE GBR HUN ITA POL Cloud computing E-sales GBR RFID Source: OECD, ICT Database; Eurostat, Information Society Statistics and national sources, July

18 Organisational change needs to be encouraged 30 Knowledge-based capital related workers, 2012 (as a percentage of total employed persons) Organisational Capital Computerised Information Design Research & Development Overlapping assets TUR SVK ITA PRT HUN ESP GRC DNK POL CZE LUX AUT IRL FIN SVN EST BEL NLD SWE DEU FRA NOR ISL GBR USA Source: OECD Science, Technology and Industry Scoreboard

19 Poor proficiency in problem solving in technology-rich environments As a percentage of year-olds (2012) Level 1 or below Level 2 Level More advanced ICT and cognitive skills to evaluate problems and solutions No ICT skills or basic skills to fullfil simple tasks Source: OECD Science, Technology and Industry Outlook 2014, based on OECD s Programme for the International Assessment of Adult Competencies (PIAAC), 19

20 FIN NZL JPN AUS DEU NLD CAN KOR GBR CHE EST BEL SVN USA IRL OECD CZE FRA SWE AUT POL ISL DNK LUX NOR SVK ITA HUN RUS PRT ESP ISR GRC TUR CHL BRA MEX IDN Science Reading Mathematics % Get the basic skills right! Science, reading and mathematics proficiency at age 15, 2009 OECD Science, Technology and Industry Scoreboard 2013, based on PISA 2009 Results: What Students Know and Can Do: Student Performance in Reading, Mathematics and Science, Vol. 1, OECD Publishing. 20

21 Science, technology, engineering and mathematics (STEM) are not enough! it s technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing (Steve Jobs) STEM need to be complemented with a broader interdisciplinary understanding of multiple complex subjects (e.g. legal & ethics) People need to rediscover their bodies as highly developed sensomotoric skills will also become a key competitive advantage 21

22 WHAT ARE THE CHALLENGES DECISION MAKERS WILL FACE?

23 Automated decision-making is not perfect! Source: Nature.com 23

24 How to improve the transparency of algorithms? Need for enhancing the transparency of automated decisions in some areas. However, transparency efforts need to respect the IPRs (incl. trade secrets) of businesses. 24

25 Need for clearer accountability and responsibility assignments Where a machine contradicts the opinion of the human decision maker, will [s]he be willing and able to take over the responsibility when overriding the machine s suggested decision? Risk of a dictatorship of data, where less educated/concerned decision makers automatically follow the decisions of machines 25

26 Thank you for your attention! Source: Chapter 6 of Data-driven Innovation: Big Data for Growth and Well-being To be presented at the OECD Forum in 2-3 June 2015 To be released in September Contact: Christian.Reimsbach-Kounatze [aatt] oecd.org 26