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05 Jeremias Prassl - HRRH congres Milaan
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05 Jeremias Prassl - HRRH congres Milaan
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
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