This is a presentation of the keynote that Lofred Madzou (AI Project Lead at the World Economic Forum) gave on October 14th at the Instituto Nacional de Defensa de la Competencia y la Propiedad Intelectual (INDECOPI) in Lima. It presents some of the most important policy challenges associated with the development of means to address them.

1. Re-thinking Regulation at the
Age of AI
Lofred Madzou, AI Project Lead at the Centre
for the Fourth Industrial Revolution
Lima, October 14th

2. 2
Outline
1. Understanding Artificial Intelligence: Definitions, Recent
Developments and Future Trajectory
2. The Biggest Challenges Facing Artificial Intelligence (AI) In
Business and Society
3. Global Policy Response to AI Development
4. Conclusion

3. Understanding Artificial Intelligence:
Definitions, Recent Developments and
Future Trajectory
3

4. 4
Understanding Artificial Intelligence
Definition
Machine learning
Machine learning techniques model
cognitive process directly from
previous experience.
They are divided into various classes
of problems:
● Supervised learning
● Unsupervised learning
● Reinforcement learning
Symbolic AI
It refers to a class of approaches that
aim to reproduce the reasoning
mechanisms of humans.These
mechanisms, put in logical form, are
produced by a priori modeling of
knowledge and rules of logic to
perform the task at hand.

5. Few examples of applications
Source : Fabernovel
Future development
● Autonomous vehicles
● Predictive medical
analytics
● Personalised learning
● Optimal energy
consumption

6. The Biggest Challenges Facing Artificial
Intelligence (AI) In Business and Society
6

7. TOP-DOWN APPROACH
… governments
can no longer design and
implement regulations
through a top-down approach
… multi-stakeholder collaboration
is needed to design
technologically informed
policies
...governments
can no longer intervene
only once harm has
occurred
… multi-stakeholder collaboration
is needed to design a more
responsive type of
regulator
….governments
can no longer pass overly
stringent regulations that
might stifle innovation
… multi-stakeholder collaboration
is needed to design
context-dependent
regulatory responses
EX-POST INTERVENTION ONE SIZE FITS ALL
SHORTCOMINGS OF CURRENT
REGULATORY FRAMEWORKS

8. 8
The biggest challenges facing AI
#1 Building Trust in AI
Multistakeholder collaboration is required to optimize explainability,
accountability and privacy to create trust.
Explainability
Investing heavily in research
for Explainable AI
Accountability
Developing the auditing
of AI to prevent bias and
discrimination
Privacy
Supporting individuals’
right to informational
self-determination

9. 9
The biggest challenges facing AI
#2 Designing Global Governance of AI
There is a pressing need to devise the global norms and institutions to
best ensure the beneficial development and use of advanced AI.
Explainability
Investing heavily in research
for Explainable AI
Privacy
Supporting individuals’
right to informational
self-determination
Technical Layer (standards)
Ethical Layer
(principles)
Social &
legal Layer
(norms &
regulation)
Ethical Layer
(principles)
Social &
legal Layer
(norms &
regulation)

10. 10
The biggest challenges facing AI
#3 Ensuring that AI industry reflects the diversity of society
Explainability
Investing heavily in research
for Explainable AI
Privacy
Supporting individuals’
right to informational
self-determination
Technical Layer (standards)
Ethical Layer
(principles)
Social &
legal Layer
(norms &
regulation)
Social &
legal Layer
(norms &
regulation)
The tech industry is overwhelmingly white
and male
Gender stereotypes affect the development
of AI

11. 11
The biggest challenges facing AI
#4 Mitigating its impact on the labour market
Explainability
Investing heavily in research
for Explainable AI
Privacy
Supporting individuals’
right to informational
self-determination
Technical Layer (standards)
Ethical Layer
(principles)
Social &
legal Layer
(norms &
regulation)
Ethical Layer
(principles)
Social &
legal Layer
(norms &
regulation)

12. The biggest challenges facing AI
#5 Ensuring a better distribution of the talent pool

13. Global Policy Response to AI
Development
13

14. 14
Global policy response to AI development
#1 Using AI to improve regulation
Rethinking collaboration between
Regulators and Academics
Source:
https://www2.deloitte.com/lu/en/pages/technology/artic
les/regtech-companies-compliance.html
Exploring the RegTech Universe

15. ● Enable automated data distribution and regulatory reporting
through big data analytics, real time reporting and cloud
● Facilitate counterparty due diligence and Know
Your Customer (KYC) procedures. AML and
anti-fraud screening and detection
Global policy response to AI development
#1 Using AI to improve regulation: Exemples
Promising Development

16. 16
Global policy response to AI development
#2 National AI Strategy Framework in action

17. 17
Global policy response to AI development
#3 Fostering a global multi-stakeholder collaboration
Key Dates
● March 2017: Creation of the first C4IR in San Francisco, USA
● July 2018: Opening of the C4IR Japan
● October 2018 :Opening of the C4IR China and India
● May 2019: Launching of the Global Councils
Goal
A platform to facilitate the co-design of
policy frameworks and advance
collaborations that accelerate the
benefits of science and technology
Impact
•Support the global governance of
technology across various jurisdictions
•Facilitate the development of
evidence-based digital policies through
our unique methodology
Along with the C4IR, an
Advisory board composed of
tech leaders from companies,
governments and universities
brings ideas and give its
recommendation on the
projets led at the Centre.
The Chairman of the advisory
Board is Marc Benioff, CEO
of Salesforce.

18. Conclusion
18

19. 19
Outline
Flounder or Flourish? The choice is ours.