Organisations need to make sure that they use AI in an appropriate way. Martijn and Hugo explain how to ensure that the developments are ethically sound and comply with regulations, how to have end-to-end governance, and how to address bias and fairness, interpretability and explainability, and robustness and security. During the conference, we looked at an example AI development process with focussing on the risks to be managed and the controls that can be established.

1. Responsible AI:
An Example of AI
Development Process
with Focus on Risks
and Controls
Brussels, 13th of February 2019

2. PwC
Agenda
2
March 2019
1. Overview of current AI guidelines
2. PwC’s Responsible AI framework
3. Practical application of AI Responsible
Framework - Governance & Control

3. PwC
Stakeholders* Before 2019 2019 2020 Beyond 2020
Europe
● European
Commission
● Council of Europe
● EBA & EIOPA
US
International level
● G20
● OECD
● ISO
● IEEE
National level
● UK
● NL
● FR
● DE
Private sector 3
EC will decide if further
regulation is necessary
Ethically Aligned Design (2016, 2017)
G20 AI Principles
Final
Draft - Principles of AI for US government
agencies
OECD Principles on AI
White paper on AI (AFM, NL regulator)
AI in the UK (2018)
FR : CNIL (2017)
GE : Declaration on AI
Continuous publications
Open AI
Microsoft
Regulation/ Law
Guidelines
Standards
Currently 10 AI ISO standards (most at stage 3-4 out of 7 (where 7 is the ISO publication)
EIOPA Expert group on
digital Ethics
Draft - Ethics
guidelines for
trustworthy AI
Update of the
Ethics
guidelines
Pilot - Ethics
guidelines for trustworthy
AI + Assessment
(*) not exhaustive
Draft Recommendation on Human
Rights Impacts of Algorithmic
Systems
GDPR & AIEuropean Civil Law rules in robotics (2016)
EBA Big data & advanced analytics
Principles
EIOPA Use of big data in Motor & Health
Several guidelines have been published in the last 2 years
SAPIBM
Google
AI Now

4. PwC
4
Source: FRA, latest update 31/12/2019
Focus on AI publications in EU
Number of AI publications by year Number of AI publications by public entity
Source: FRA, latest update 31/12/2019

5. PwC
To accelerate innovation and fully realise the potential of AI, responsible
and ethical considerations need to be prioritised
5
ETHICS & REGULATION : Assess risks related to ethical aspects of
AI and operationalise ethical AI for the respective context; identify
and evaluate relevant regulations that impact AI solutions
INTERPRETABILITY & EXPLAINABILITY : Explain model decision
making overall and what drives an individual prediction to different
stakeholders
BIAS & FAIRNESS : Uncover bias in the underlying data and model
development process and enable the business to understand what
process may lead to unfairness
Ethical & societal
CONTROL & GOVERNANCE : Introduce enterprise-wide and
end-to-end accountability for AI applications and consistency of
operations to minimise risk and maximise ROI
Performance & security
Control & Governance
ROBUSTNESS & SECURITY : Assess the performance of AI over
time to identify potential disruptions or challenges to long term
performance
BIAS &
FAIRNESS
INTERPRETABILITY
& EXPLAINABILITY
ROBUSTNESS
& SECURITY

6. PwC 6
Let’s focus today on the dimension
“Control & Governance”
Ethical & societal
Performance & security
Control & Governance
BIAS &
FAIRNESS
INTERPRETABILITY
& EXPLAINABILITY
ROBUSTNESS
& SECURITY
Will your AI behave as intended?
An AI system that does not demonstrate stability, and
consistently meet performance requirements, is at
increased risk of producing errors and making the wrong
decisions.
To help make your systems more robust, Responsible AI
includes services to help you identify weaknesses in
models, assess system safety and monitor long-term
performance.

7. PwC 7
Let’s take a practical case

8. PwC 8
In order to avoid the same issues, PwC’s Responsible AI
governance and controls cover an end-to-end AI journey

9. PwC 9
Solutions design
Pre-processing
Model building
4 Development
Let’s take the “Business & data understanding” step
and further detail it into smaller sub-steps
Internal
datasets
External
datasets
Explore data &
check data quality
Perform tests to
identify potential bias
Data extraction
Bias is specific for AI.
Goldman Sachs should
use reasonable efforts
to ensure that the
datasets used for AI
model training are
adequate for the
intended purpose,
assess the risk of bias.
Data exploration is a
common step for data
scientists.
It’s key to ensure
accuracy,
completeness,
consistency
timeliness, etc. of
your dataset
Typical external
dataset for credit
loans includes
information about
blacklisted people
with historical
credit issues
Goldman Sachs
used internal
datasets with
different features
e.g. location, age,
income, number of
open credits, type of
profession, etc.
Business and data
understanding

10. PwC 10
A typical risk & control approach (tailored to AI) could be
applied to cover both traditional and AI-specific risks
Internal
datasets
Risk
Identification
Legal risks : GDPR/
Privacy
Legal risk :
Copyright
Lack of data quality
(DQ) i.e. completeness,
accuracy, etc.
Unintended
discriminatory decisions
Risk
assessment
Controls
Pre-validation by
the DPO /
compliance office
Pre-validation by
the compliance
officer
Document data quality
assessment according
a defined methodology
Execute bias detection
methodology or tools
Monitor
Signed document Signed document Reporting with DQ
checks results
Documented bias
checks results
External
datasets
Explore data &
check data quality
Perform tests to
identify potential bias
AI specific risks

11. PwC 11
Some examples of metrics to detect potential bias on a
given population (e.g. gender discrimination)

12. PwC
1. Consider the end-to-end process
2. Assign clear roles and responsibilities to people involved in AI
3. Re-use existing risk management framework of your organization and tailor
it to AI specific risks
12
Key Takeaways

13. PwC
Find out more about how your organisation may benefit from
Responsible AI
13
Access the PwC Responsible AI website for the latest
materials and resources.
Take the free Responsible AI Diagnostic to understand
the maturity of your organization’s Responsible AI.

14. Thank you
© 2020 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see
www.pwc.com/structure for further details.
www.pwc.be
14
Martijn Cuypers
Director
Insurance Practice Belgium
+32 475 55 69 54
martijn.cuypers@pwc.com
Hugo Pires
Senior Manager
Insurance Practice & AI services
Belgium
+ 32 472 87 81 21
hugo.pires@pwc.com