Data-driven innovation - European Parliament · 2015-06-02 · Poor ICT adoption in many...

DATA-DRIVEN INNOVATION and the implications on jobs and skills

Christian.Reimsbach-Kounatze@oecd.org http://oe.cd/bigdata

Working Group on Legal Questions related to the

Development of Robotics and Artificial Intelligence

26 May 2015

DDI refers to the use of data and analytics

to improve or foster new products,

processes, organisational methods and

markets

2

What is Data-Driven Innovation (DDI)?

Big data feeding ML algorithms to

enable autonomous decision making

3

Value added

growth

and well-being

Knowledge base

Decision

making

Datafication and

data collection

Big data

Data value cycle

Data analytics

(machine learning, ML)

DDI enables next generation

autonomous machines and systems

Manufacturing

Agriculture

Finance 4

Logistic

Health

Transportation

5

Example: algorithmic trading in finance

Algorithmic trading as share of total trading

Note: 2013-14 based on estimates.

Source: OECD based on The Economist (2012) and Aite Group

• 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

IoT will be the next game changer

7

Towards the next production revolution?

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

What are the employment implications?

WHO WILL BE THE LOSERS AND WINNERS?

10

We have to learn from history!

Jacquard loom punch cards

Handmade damasks

Mechanical tabulator

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

Capacities needed to successfully

“race against the machines”

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, 2013

13

Implications on inequalities …

Trends in wages for full-time, full-year male workers in the United States,

1963-2008

Source: Brynjolfsson and McAfee, 2014 based on Acemoglu and Autor (2011)

14

… with the share of income

going to labour declining steadily.

0.55

0.60

0.65

0.70

0.75

0.80

1980 1990 2000 2010

Australia Germany Japan United States

Source: OECD Unit Labour Costs – Annual Indicators

15

What used to be attributed to labour is

now knowledge-based capital.

• IP

• Software

(e.g. ERP,

algorithms)

• Data (“Big”)

• Creativity

• Expert decision making

• Organisational know-how

• Marketing, sales, customer

relations

Capital Labour

Ownership of autonomous machines and

systems will be defined by IP rights

DO WE HAVE THE CAPACITY TO

“DANCE WITH THE MACHINE”?

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

Source: OECD, ICT Database; Eurostat, Information Society Statistics and national sources, July 2014.

17

FINFIN

NZL

ISL

SWEPRT FIN

NZL

KOR

GRC

TUR

TUR CZEGBR HUN

POL

ITA

GBR0

20

40

60

80

100

Broadband Website E-purchases Social network ERP Supply chainmngt. (ADE)

Cloud computing E-sales RFID

%

Highest Lowest 1st and 3rd quartiles Median Average

Knowledge-based capital related workers, 2012

(as a percentage of total employed persons)

18

Organisational change needs to be

encouraged

Source: OECD Science, Technology and Industry Scoreboard 2013.

http://dx.doi.org/10.1787/888932890618

0

5

10

15

20

25

30

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

Organisational Capital Computerised Information Design Research & Development Overlapping assets

19

Poor proficiency in problem solving in

technology-rich environments

100

80

60

40

20

0

20

40

60

80

100

Level 1 or below Level 2 Level 3

No ICT skills or basic skills to fullfilsimple tasks

More advanced ICT and cognitive skills to evaluate

problems and solutions

Source: OECD Science, Technology and Industry Outlook 2014, based on OECD’s Programme for the

International Assessment of Adult Competencies (PIAAC), http://dx.doi.org/10.1787/888933151932.

As a percentage of 16-65 year-olds (2012)

20

Get the basic skills right!

05

10

15

20

25

30

FIN

NZ

L

JP

N

AU

S

DE

U

NL

D

CA

N

KO

R

GB

R

CH

E

ES

T

BE

L

SV

N

US

A

IRL

OE

CD

CZ

E

FR

A

SW

E

AU

T

PO

L

ISL

DN

K

LU

X

NO

R

SV

K

ITA

HU

N

RU

S

PR

T

ES

P

ISR

GR

C

TU

R

CH

L

BR

A

ME

X

IDN

%

Scie

nce

Re

ad

ing

Ma

the

ma

tics

Science, reading and mathematics proficiency at age 15, 2009

OECD Science, Technology and Industry Scoreboard 2013, http://dx.doi.org/10.1787/888932890675

based on PISA 2009 Results: What Students Know and Can Do: Student Performance in Reading,

Mathematics and Science, Vol. 1, OECD Publishing.

• “… 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

Science, technology, engineering and

mathematics (STEM) are not enough !

WHAT ARE THE CHALLENGES DECISION MAKERS

WILL FACE ?

Automated decision-making is not

perfect!

23 Source: Nature.com

• Need for enhancing

the transparency of

automated decisions

in some areas.

24

How to improve the transparency of

algorithms?

• However, transparency efforts need to respect the IPRs (incl. trade secrets) of businesses.

• 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

Need for clearer accountability and

responsibility assignments

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 2015

• http://oe.cd/bigdata

Contact: Christian.Reimsbach-Kounatze [aatt] oecd.org