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Responsible AI: An Example AI Development Process with Focus on Risks and Controls, presented by Martijn Cuypers, Insurance Director and Hugo Pires, Senior Manager @ PwC) at the Trustworthy and Ethical AI conference on Feb. 13th

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.

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

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Responsible AI: An Example AI Development Process with Focus on Risks and Controls, presented by Martijn Cuypers, Insurance Director and Hugo Pires, Senior Manager @ PwC) at the Trustworthy and Ethical AI conference on Feb. 13th

  • 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