1) Technological advancement and automation have long raised concerns about technological unemployment, with thinkers like Ricardo, Keynes, and Friedman addressing this issue. However, other factors also influence employment levels. 2) Recent developments involve the rise of algorithmic management in the workplace through tools like people analytics, which use digital monitoring and machine learning to collect workplace data and automate or enhance management decisions. 3) Regulating this new form of AI-powered people management raises issues around fairness, discrimination, privacy, responsibility, and transparency that will need to be addressed to manage risks while seizing opportunities.

1. THE
ALGORITHMIC
BOSS
@JeremiasPrassl

2. Technological
Change & the
Future of Work
A (brief) history of1

3. Technological
Unemployment
3
An idea with a long pedigree…
Ricardo (1820s)
Keynes (1930s)
Friedman (1960s/70s)
Frey & Osborne / Brynjolfsson & McAfee (2000s)

4. … but don’t ignore
other factors
Adams (2018): the key role of tech in
overcoming imperfect information
[Production: gig economy]
Hiring
Monitoring
4

5. Algorithmic
Management
The Rise of2

6. From Gig Work…
6

7. … to People Analytics
7
in WORKPLACES
across
the SPECTRUM
Increasing
AUTOMATION
of the full range of
MANAGEMENT
FUNCTIONS

8. … to People Analytics
8

9. … to People Analytics
9

10. AI at WORK
Understanding
3

11. Scientific
Management
11
TAYLORISM,
a century on?

12. This time…
… it’s different
12

13. This time…
… it’s different
13
1
IN
FO
RM
A
TIO
N
From
digitalm
onitoring
to
self-tracking

14. This time…
… it’s different
14
1
IN
FO
RM
A
TIO
N
From
digitalm
onitoring
to
self-tracking
2
PRO
C
ESSIN
G
M
achine
Learning
(‘AI’)

15. Collection
Digital
Physical
Self-Tracking
DATA
15

16. Collection
Digital
Physical
Self-Tracking
… at work, and
beyond
DATA
16

17. Collection
Digital
Physical
Self-Tracking
… at work, and
beyond
DATA
Structure
What can be
measured?
17

18. Collection
Digital
Physical
Self-Tracking
… at work, and
beyond
DATA
Structure
What can be
measured?
And how is it
structured?
18

19. ‘AI’
Developing
Machine
Learning to
Enhance and/or
Automate
Decision Making
PROCESSING
19

20. ‘AI’
Developing
Machine
Learning to
Enhance and/or
Automate
Decision Making
PROCESSING
Outcomes
From Evaluation
to New Forms of
Management
Control
20

21. REGULATING AI
MANAGEMENT
Seizing Opportunities,
Managing Risks
4

22. Efficiency
Objectivity
Neutrality
Clear Potential…
22

23. Tackling
Discrimination
Clear Potential…
… and Downsides
23

24. Urgent need to develop and/or rethink our
notions of
Fairness
(Legal) Responsibility
Clear Potential…
… and Downsides
24

25. Privacy & Data Protection
Consent?
Discrimination Law
Patterns in Training Data
DATA
25

26. Algorithmic Explicability
How are decisions taken?
Ascribing Responsibility
Grappling with new forms of control
PROCESSING
26

27. FUTURE OF
WORK
ARTIFICIAL INTELLIGENCE
AND THE
5

28. 28
Source: Amazon / USPTO

29. 29
@Jeremias Prassl
University of Oxford

30. Credits
Presentation template by SlidesCarnival
Photographs by Unsplash
30