Dr. Anand S. RaoGlobal AI Lead, PwC

Risks of AI & Responsible AI

GLOBAL ARTIFICIAL INTELLIGENCE LEADDr. Anand S. Rao

www.pwc.com

PwC– AI Lab

Today’s discussionExcitement Around AI

AI based Automation and Augmentation: Finance & Accounting

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Risks of AI03

PwC – AI Lab

Excitement around AI

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”In a way, AI is both closer and farther off than we imagine. AI is closer to being able to do more powerful things than

most people expect -- driving cars, curing diseases, discovering planets, understanding media. Those will each

have a great impact on the world, but we're still figuring out what real intelligence is.”

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Mark Zuckerberg, "Building Jarvis", Facebook, December 19, 2016

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AI is closer than you think…

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Deep Blue beats Garry KasparovMAY 11, 1997

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FEB 16, 2011

Watson beats Jeopardy Champions

PwC – AI Lab

AlphaGo beats Lee Sedol

MARCH 15, 2016

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PwC – AI Lab

AlphaGo v. AlphaGo Zero vs AlphaZero (2016-2017)

• Trained with data from human Go Players

• Uses data from playing with itself

• Generated ’new’ moves that humans had not used

• AlphaGo Lee beats Go Grandmaster Lee Sedol 4-1 in March 2016

• Uses just the rules of the game with no human data

• AlphaGo Zero beats AlphaGoLee in 3 days of training in 2017

• The same program now uses the rules of Chess

• AlphaZero AI beats Stockfish(best Chess program) 64-36

• System was trained in 4 hours using 5,000 TPUs

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AI is farther than you think…

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Deep Learning is NOT EQUAL to Deep Understanding

Alexa: What is the weather?

The weather in Lexington is …

Alexa: What was my previous

question?

Here is something I found on Dictionary.com. Previous question…

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Two Paths to AIEnterprises are realizing value along two distinct paths from Digitization to AI

Digitization

Artificial Intelligence

Productivity Experience Profits

Simplification

Standardization

Automation

Revenues

Autom

ation P

ath

An

alytics Path

Data (Volume, Velocity, Variety, Veracity, Value)

Cognification

Personalization

Analytics

PwC – AI Lab

Statistics Econometrics Optimization Complexity Theory

Computer Science

Game Theory

FOUNDATION LAYER 13

AI that can act…▪ Robotic process automation

▪ Deep question & answering

▪ Machine translation

▪ Collaborative systems

▪ Adaptive systems

AI that can sense…▪ Natural language

▪ Audio & speech

▪ Machine vision

▪ Navigation

▪ Visualization

AI is defined as the theory and development of systems that sense the environment, make decisions, and act that would normally require human intelligence.

Hear

SeeSpeakFeel

AI that can think…▪ Knowledge & representation

▪ Planning & scheduling

▪ Reasoning

▪ Machine Learning

▪ Deep Learning

Physical

Creative

Cognitive

ReactiveUnderstand

PerceivePlan

Assist

PwC – AI Lab

AI based Automation and Augmentation: Finance & Accounting

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PwC – AI Lab

AI is being applied in four distinct ways progressing from automated to assisted to augmented to autonomous intelligence

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Automated Intelligence1

Assisted Intelligence

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Augmented Intelligence3

Autonomous Intelligence4

Hardwired / specific systems

Adaptivesystems

No human in the loop

Human in the loop

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PwC – AI Lab

Finance in the digital age will deliver better value with reduced resources and will also be a proactive partner to business

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Advances in automation and augmentation technology offer Finance the opportunity to move into more value-adding roles by improving its own operations as well as bringing technological advances to the business

Challenges?

Introducing pioneering technology to reduce costs and increase efficiency to the rest of the organisation

Improving quality and control of transactional activity using a digital workforce

Bringing forward-looking ‘cockpits’ to business to perform ‘what-if’ analysis

Improving strategic and financial planning for disruptive and transformational initiatives

PwC – AI Lab

So what is artificial intelligence in a business context and what are its limitations?

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Find patterns in apparently random data or apply structure to unstructured data

Planning and thinking ahead

Learn through repeated exposure to particular problems

And what it can’t do….( not yet anyway) …What AI can do…

Make sense of human communication (speech or text), interpret and identify rich media (e.g.. music and images)

It can’t place its work in context i.e. see the bigger picture

Context aware

It’s only as good as the data it’s been trained on

Self-improvement

It cannot reason from first principles

Common sense reasoning

Its capability is limited to the purpose for which it is built

Multi-tasking

PwC – AI Lab

Augmentation of finance role by AI technologies – natural language processing, machine learning, and intelligent agents

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Producing reports and narratives with data driven insights

Extracting and processing data from multiple sources and formats such as invoice processing

What-if analysis of financial and

operational goals

Detection of fake articles or journals for

publication, or suspicious internal

activity

Reporting and Insight

Business Model Simulation

Smart Extraction

Fraud Detection

PwC – AI Lab

Case Study: Use of NLP and Machine Learning in document processing

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PwC – AI Lab

Case Study: Business model simulation – Finance in M&A and Partnerships

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Pilot

Build

Scale

StrategyPwC assists the Director of Strategic Initiatives of a large Global Auto Manufacturer, in evaluating the ride share/car share business model and potential disruptors in the personal mobility sector

Go-to-Market ModelPwC uses advanced AI techniques (agent-based simulation, big data technologies) to model over 200K go-to-market scenarios modelling consumer behaviour, pricing, adoption, and competitive response

City SelectionClient decides to set up a separate business unit focused on Personal Mobility. PwC assists in city selection model and go-to-market strategy for each city

Acquisitions & PartnershipsClient leverages the personal mobility adoption model to make targeted acquisitions and partner with new Auto-tech players in different cities

AV Operational ModelPwC assists Client in developing an operational model for autonomous vehicles and electric vehicles involved in Personal Mobility

Operational RolloutClient is rolling out personal mobility services in major US cities and aims to collect additional operational data to feed the personal mobility models

EU ExpansionPwC is assisting the Client’s European Division to evaluate and select cities for expanding personal mobility services

PwC – AI Lab

Risks & Challenges of AI

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Large organizations and regulators have been voicing concerns about the risks associated with AI and the importance of understanding AI ‘Black Box’

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PwC – AI Lab

AI risks that need to be assessed, mitigated and managed can be categorized into six categories that impact consumers, businesses, societies and nations

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Performance• Risk of errors• Risk of bias• Risk of opaqueness• Risk of performance instability

Security• Adversarial attacks• Cyber intrusion risks• Privacy risks• Open source software risks

Ethical• Lack of values risk • Value alignment risk

Societal• Reputational risk• Autonomous weapons

proliferation• Risk of intelligence divideEconomic

• Job displacement• Liability risk • Risk of “winner takes all”

concentration of power

BUSINESS-LEVEL RISKS

NATIONAL-LEVEL RISKS

Risks

• Lack of human agency in AI supported processes

• Inability to detect/control rogue AI

Control

PwC – AI Lab

PwC’s Responsible AI toolkit covers the five fundamental aspects that make AI responsible

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ETHICS & LEGAL Ensure AI development is in line with major local and global regulations, both enacted and emerging; and allow the business to evaluate the ethics of an AI system and how to operationalize ethics in the organization

INTERPRETABILITYEnable human users to understand, appropriately trust, and effectively manage the emerging generation of AI

BIAS & FAIRNESSUncover bias in the underlying data and model development process and enable the business to understand what process may lead to unfairness

ROBUSTNESS & SECURITY Assess the performance of AI over time to identify potential disruptions or challenges to long term performance

GOVERNANCEIntroduce enterprise-wide and end-to-end accountability for AI applications and consistency of operations to minimize risk and maximize ROI

Ethical & Societal

Performance & Security

Control

PwC

Apply Ethics & Legal throughout

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...helping clients understand the ethical implications of their use of AI

Business Implications

• Ensure AI development is in line with major local and global regulations, both passed and in discussion.

• Allow the business to evaluate the ethics of an AI system and the impact to employees and customers.

• Enable the business to deploy AI with confidence.

How it will operate

• The general and contextualized principles will help in defining the organization’s AI strategy, as well as guiding the development and operations of AI models

• The Legal framework Repository of discussed and passed regulation by territory and industry

Ethical Principles Traceability Matrix

Full Coverage Partial Coverage No Coverage n Principle ID

PwC

Ensure end-to-end Governance

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...ensuring auditability through the allocation responsibility, accountability and controls for AI

Business Implications

• Introduce enterprise-wide accountability for AI applications and consistency of operations.

• Enable and empower clients to effective control and manage AI applications across the business.

How it operates

• Standardized controls framework

• Centralized dashboard for monitoring of AI across the organization

• Auditable documentation of data, model, and human interaction with AI

Model Level

1. Strategy

3. Ecosystem

5. Deployment

6. Operate and Monitor

Corporate Strategy

Industry Standards & Regulations

Internal Policies & Practices

Operational Support

Compliance

Portfolio Management

Program Oversight

Delivery Approach

Technology Roadmap

Sourcing

2. P

lan

nin

g

Transition & Execution

Ongoing monitoring

Data Extraction

4. D

evel

opm

ent

Change Management

Evaluation & Check-in

Model Integration

Solution Design

Business & Data Understanding

Pre-Processing

Model BuildingWho Benefits:• C-Suite• Risk & Compliance• Process Owners• Data Scientists• Consumers• Regulators

PwC

Uncover potential Bias Business Implications

• Uncover bias risks in the underlying data and model development process.

• Enable the business to understand what influences a model’s decision that could lead to unfairness to the individual or the group.

How it operates

• Bias framework and diagnostic to identify potential concerns in the data, model development, and usage process

• Automated assessment of data and model bias, customized to the needs of the organization, including detection and correction of proxies.

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...identifying aspects of data, model, and human interaction that lead to unfairness of AI

Statistical ParityConditional Statistical

Parity

Predicted & Actual Outcomes

Test FairnessWell-Calibration

Balance for Positive ClassBalance for Negative Class

Statistical Measures

True PositivesFalse PositivesFalse NegativesTrue Negatives

Predictive ParityFalse Positive Error Rate BalanceFalse Negative Error Rate Balance

Equal OpportunityConditional Accuracy

Overall AccuracyTreatment Equality

Positive Predictive ValueFalse Discovery RateFalse Omission RateNegative Predictive

Value

True Positive RateFalse Positive RateFalse Negative RateTrue Negative Rate

Similarity-based MeasuresCausal Discrimination

Fairness Through Unawareness

Fairness Through Awareness

Causal DefinitionsCounterfactual Fairness

No Unresolved Discrimination

No Proxy DiscriminationFair Inference

Predicted Outcomes

Predicted Probabilities

Decision

Fairness Definition

Protected Attributes

Dataset

PwC

Uncover potential Bias Business Implications

• Uncover bias risks in the underlying data and model development process.

• Enable the business to understand what influences a model’s decision that could lead to unfairness to the individual or the group.

How it operates

• Bias framework and diagnostic to identify potential concerns in the data, model development, and usage process

• Automated assessment of data and model bias, customized to the needs of the organization, including detection and correction of proxies.

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...identifying aspects of data, model, and human interaction that lead to unfairness of AI

Data Reporting

Model Bias Detection

Bias Intervention

Input data User can upload dataset on the platform or leverage synthetically generated data

Users can perform different exploratory data analysis

Distributions analysis

Multicollinearity

Outliers detection

Missing values analysis

Identifying Proxies

Choose bias intervention techniques

Threshold computation to achieve individual or

group fairness

Leverage fair algorithms that optimize accuracy by

weighing in fairness

Tuning the effect of Proxies on the model by

various techniques

Creates a report that generates summary details of class imbalance and missing values, along with identifying proxy variables

Generates a report summarizing the success/failure details of sensitive attributes by fairness measures.

PwC

Introduce Interpretability Business Implications

• Introduce explainability and interpretability to AI systems, while maintaining a high level of learning performance.

• Enable human users to understand, appropriately trust, and effectively manage the emerging generation of AI.

How operates

• Global Interpretability reports help data scientists to understand black box models

• End-users are provided with comprehensible reports explaining how their data was used to reach a decision

• Automated exploration of decision boundaries, including explanations of what needs to change in order to shift a prediction

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...helping clients increase their understanding of AI systems and tackle the black box problem

Scratch Dent Crack

Panel Separation Missing Piece Non-damaged

Possible Damaged Parts:Detected Parts:

PwC

Robustness & Security

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...safeguarding an AI system by through long-term stability and high performance Business Implications

• Assess the performance of AI over time to identify potential disruptions or challenges to long term performance.

• Enable the business to gain confidence in AI through simulation.

How it operates

• AI benchmarks to assess long term performance

• Real-time and continuous monitoring of model decision making and flagging of deviated activity

• Sensitivity analysis & stress testing

Synthesize

Perturb

AI Model

Input Data &Specify the Structure

Learn the Data

Test Model Robustness

Test Model Sensitivity

PwC 31

Are we entering a new AI-inspired arms race?

PwC 32

The key elements of National AI Strategies must address six policy categories

Reskilling• Workforce reskilling• Digital fitness• University education

Basic AI R&D• Moonshot projects• University funding• Business incentives

Business Protection• Local companies• Specific industry sectors• Algorithmic governance

Specialized AI Tech.• Drones• Autonomous vehicles• Service robots

Consumer Protection• Data security• Income security• Digital anonymity

Ethics• Citizen monitoring• Autonomous weapons• Beneficial use of AI

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PwC | A practical guide to responsible AI – Interpretability & ExplainabilityPwC’s Digital Services

Thank you.

© 2019 PwC. All rights reserved. Not for further distribution without the permission of PwC. “PwC” refers to the network of member firms of PricewaterhouseCoopers International Limited (PwCIL), or, as the context requires, individual member firms of the PwC network. Each member firm is a separate legal entity and does not act as agent of PwCIL or any other member firm. PwCIL does not provide any services to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms nor can it control the exercise of their professional judgment or bind them in any way. No member firm is responsible or liable for the acts or omissions of any other member firm nor can it control the exercise of another member firm’s professional judgment or bind another member firm or PwCIL in any way.

Dr. Anand S. RaoGlobal AI Lead

anand.s.rao@pwc.com@AnandSRao