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Responsible AI
- 1. PwC AI Lab| 1 Responsible AI – Role of Consumers, Businesses, and Governments Dr. Anand S. Rao Global Artificial Intelligence Lead
- 2. PwC AI Lab| 2 Today’s discussion Enterprise AI Through Four Lenses Enterprise AI Case Studies Risks of AI 01 02 03 Responsible AI04
- 3. PwC AI Lab| 3 01 Enterprise AI Through Four Lenses
- 4. PwC AI Lab| 4 AI as Sense-Think-Act Sense Artificial Intelligence is becoming ubiquitous intelligence with the ability to see, hear, speak, smell, feel, understand gestures, interface with your brain, and dream Think AI is helping us do tasks faster, better and cheaper – Automated Intelligence; helping us make better decisions – Assisted & Augmented Intelligence, or even taking over what we do – Autonomous Intelligence Act Artificial Intelligence is equaling or surpassing humans in a number of other tasks – playing games, driving cars, recommendations (movies, books, finance, research), etc.
- 5. PwC AI Lab| 5 Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATION LAYER Sense Think Act • Robotic process automation • Deep question & answering • Machine translation • Collaborative systems • Adaptive systems • Knowledge & representation • Planning & scheduling • Reasoning • Machine Learning • Deep Learning • Natural language • Audio & speech • Machine vision • Navigation • Visualization AI that can sense… AI that can think… AI that can act… Hear See Speak Feel Understand Perceive PlanAssist Physical Creative Cognitive Reactive More Formally…
- 6. PwC AI Lab| 6 Business Lens Metrics & Value Chain Intelligence Lens Automated, Assisted, Augmented & Autonomous Data Lens Structured vs Unstructured Available vs Augmented Technology Lens Techniques, Tools & Platforms Four Lenses of Artificial Intelligence
- 7. PwC AI Lab| 7 Business Lens: Metrics & Value Chain Operations & Development Product Development Service & Support Operations Outbound Logistics Sales & Distribution Customers & Marketing Strategy & Growth Supply Chain & Procurement Finance, HR, Planning Inbound Logistics How will we ensure our product supply is meeting demand? VP, Supply Chain How can we engage with our customers to enhance their experience? Director, Marketing How can we grow our market share and which markets to enter, exit or expand? Director, Strategy How do we innovate and introduce new products and services? Director, Products How do we increase customer satisfaction and retain more customers? Director, Service How can we reach more customers and price our products to increase sales? Director, Sales How can we increase efficiency and effectiveness of our operations? Director, Operations How can we get a better return on our talent, capital, and assets? Director, Finance & HR • Market Share • Customer Experience • Acquisition Rate • Innovation Rate • Operational Efficiency • Customer Satisfaction • Talent Retention • Inventory Turn Over 300+ AI Use Cases Across 8 Sectors – Sizing the Prize
- 8. PwC AI Lab| 8 Intelligence Lens: Four Types of Enterprise AI No human in the loopHuman in the loop Hardwired / specific systems Adaptive systems Automated Intelligence 1 Assisted Intelligence 2 Augmented Intelligence 3 Autonomous Intelligence 4 +
- 9. PwC AI Lab| 9 Data Lens: Four Types of Data Structured AvailableAugmented Unstructured
- 10. PwC AI Lab| 10 What is Artificial Intelligence? Artificial Intelligence can be defined as the theory and development of systems that can continuously sense its environment, think, make decisions, and take actions that influence the environment to achieve its goals. Technology Lens: AI Techniques Machine Vision Natural Language Audio & Speech Navigation Visualization SENSORY LAYER Knowledge Representation Reasoning Planning & Scheduling Machine Learning Deep Learning COGNITIVE LAYER Robotic Process Automation Deep Question & Answering Machine Translation Collaborative Systems Adaptive Systems BEHAVIORAL LAYER Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATIONAL LAYER
- 11. PwC AI Lab| 11 02 Enterprise AI Case Studies
- 12. PwC AI Lab| 12 Case 1: Global Pharmaceutical Case 2: Construction Company Case 3: Automotive Manufacturer Case 4: Digital Advisor
- 13. PwC AI Lab| 13 Global Pharmaceuticals Extracting adverse drug interaction from clinician notes, social media, and medical literature to enhance productivity and effectiveness (96% accuracy)
- 14. PwC AI Lab| 14 Adverse Event Pipeline using NLP Toolkit
- 15. PwC AI Lab| 15 Deep Learning of Latent Relationships Word2Vec is able to show the relationship between Sneezing and Anti- histamine.
- 16. PwC AI Lab| 16 AI in Healthcare
- 17. PwC AI Lab| 17 Digital Advisor Gamification of Strategy resulted in the development of a digital advisor that simulates household level (128 million) financial data into the future to enhance financial wellness PwC AI Lab | 17
- 18. PwC AI Lab| 18 $ecure is a digital advice and financial wellness toolkit, that enables a differentiated digital advice experience for customers in a cost-efficient manner 01 02 03 Synthetic dataset of 1.28M U.S. households with 4000+ data points Personalized customer experience by life stage Agent-based model to project household finances 01 02 03 “Households Like You” benchmarking for consumer education/data augmentation Holistic retirement planning using advanced scenario analysis Intuitive planning tools and what-if analysis that demystify the planning process Core Components Key Differentiators
- 19. PwC AI Lab| 19 Key Differentiator #1: “Households Like Yours” matching to enable benchmarking/data augmentation Client’s Name * Illustrative John Doe Smith Household Zip Code 75220 Gender Male Marital Status Married # Dependents 2 Annual Base Income Total Assets Tell us a little about yourself … We’ll benchmark you against peer households … $1,650 $1,750 $765 $650 $885 $1,100 Your Household Households Like Yours Household Balance Sheet ($ ‘000) Total Assets Liabilities Net Worth $365 $350 $220 $165$145 $185 Your Household Households Like Yours Household Income Statement ($ ‘000) Income Expenses Surplus/Deficit … and help you augment missing/incomplete data Co-Client’s Name Mary Jo Smith Co-Client’s Age 45 Age 47 Co-Client’s Annual Base Income i Households Like Yours: $175K - $195K PwC Synthetic Dataset “Households Like You” estimates increase in accuracy as more data points become available
- 20. PwC AI Lab| 20 Key Differentiator #2: Retirement Planning Evolved - Holistic cross-silo perspective on current and future assets and liabilities with advanced scenario analysis 20 Rather than having to monitor multiple metrics, users only track fundedness, which takes stock of current and future assets and liabilities Others: Incomplete retirement readiness representation vs. Picture source: Betterment.com Limited guidance on how much to save, due to absence of the liabilities side of the equation Basic scenario analysis focused primarily on asset growth across multiple economic environments * Illustrative 0% 20% 40% 60% 80% 100% 120% 140% 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Fundedness(%) Age – Head Of Household (J. Smith) Projected Fundedness To Retirement Pessimistic Expected Emergency Healthcare (Client) College Tuition (Elder Child) Constraine d OverfundedUnderfunded Long-Term Care (Spouse) In addition to macroeconomic factors, $ecure features sophisticated scenario analysis that captures significant life events as well $ecure: Holistic retirement readiness monitoring
- 21. PwC AI Lab| 21 PwC’s Digital Services Six success factors to derive maximum benefits from artificial intelligence Start from business decisions 01 Demonstrate value through pilots before scaling 02 Blend intuition and data-driven insights 03 Address ‘big data’ – don’t forget ‘lean’ data 04 Fail forward – test and learn culture 05 Focus on Responsible AI from the start 06
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- 23. PwC AI Lab| 23 Risks of AI “The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.” — Stephen Hawking “I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.” — Elon Musk
- 24. PwC AI Lab| 24 Recent Fatality from a autonomous vehicle It happened at 10 p.m. in Tempe, Arizona, where ride-hailing company Uber had been picking up passengers in autonomous vehicles for more than a year. Elaine Herzberg, 49, was walking her bicycle down a four-lane road and was starting to cross when the gray Volvo, operated by Uber, hit her at about 40 mph, according to local police.
- 25. PwC AI Lab| 25 Control Risk of AI going ‘rogue’ Inability to control malevolent AI Swarm drones Performance Risk of Errors Risk of Bias Risk of Opaqueness Risk of stability of performance Lack of feedback process Security Cyber intrusion risks Privacy risks Open source software risks Digital, Physical, Political security Robust AI: Performance, security and control risks
- 26. PwC AI Lab| 26 Software Risks: Bias Risk – How can we avoid data bias in recommendations? COMPAS, a system used by US Judges to forecast which criminals are likely to reoffend was biased. It concluded that almost “blacks are almost twice as likely as whites to be labeled a higher risk but not actually re- offend.” COMPAS
- 27. PwC AI Lab| 27 Security Risks: Cyber Intrusion risk– How can we prevent ‘cyber’ intrusion of automated or electronic vehicles? After hackers Charlie Miller and Chris Valasek hacked the Jeep Cherokee and stopped the car off the highway, Chrysler issued a 1.4 million vehicle recall and mailed USB drives with software updates to affected drivers. Simulated ‘Cyber Intrusion’
- 28. PwC AI Lab| 28 Control Risks: ‘Rogue’ risk– How can we ensure that an AI designed with benevolent intent does not go ‘rogue’? Tay, a Microsoft chatbot, released to interact with the public began tweeting racist and inflammatory remarks in under 24 hours and had to be decommissioned. Tay Chatbot
- 29. PwC AI Lab| 29 Societal Risk of Autonomous Weapons proliferation Risk of ‘intelligence divide’ Ethical ‘Lack of Values’ risk Value Alignment risk Goal Alignment risk Economic Job displacement risks ‘Winner-takes-all’ concentration of power risk Liability risk Beneficial AI: Ethical, economic, and societal risks
- 30. PwC AI Lab| 30 Ethical Risks – How can a autonomous vehicle learn the ’value’ of human life? Should the AV continue and (definitely) kill one pedestrian who is disobeying the law? Or should the AV swerve and (potentially) kill two pedestrians who are obeying the law? MIT’s Moral Machine MIT’s Moral Machine allows users to select scenarios to understand human ethics to determine what the ‘machine ethics’ should be
- 31. PwC AI Lab| 31 Economic Risks – How can we manage job losses due to automation from becoming a major economic issue? Automation Job Losses A number of studies are predicting job losses, up to 50% or more, from automation in different sectors in different geographies.
- 32. PwC AI Lab| 32 Societal Risks – How can we ban the proliferation of autonomous weapons designed to ‘kill’? Autonom0us Weapons Proliferation Source: Why we should really ban Autonomous Weapons, Stuart Russell, Max Tegmark, and Toby Walsh, August 3, IEEE Spectrum, 2015
- 33. PwC AI Lab| 33 04 Responsible AI
- 34. PwC AI Lab| 34 Responsible Artificial Intelligence We define Responsible Artificial Intelligence, as the combination of building Robust AI systems that will engender ‘trust’ in today’s AI system as well as work towards the development of AI that will be beneficial to society today and in the future. Robust Artificial Intelligence, is concerned with the verification, validation, security and control of AI systems Beneficial Artificial Intelligence, is concerned with maximizing the social benefit of AI • Reduce or eliminate software risks • Reduce or eliminate security risks • Reduce or eliminate control risks • Reduce or eliminate economic risks • Reduce or eliminate societal risks • Reduce or eliminate ethical risks
- 35. PwC New Services| 35 Robust Artificial Intelligence • Verification: Modular agent-based architectures; verifiable substrates of operating systems and platforms; adaptive control theory; and deep learning theory • Validation: Computational models of ethical reasoning; goal stability; reasoning under uncertainty; and bounded rationality • Security: Software, hardware, and psychological containment; tripwires – detection and response; detecting intent to deceive. • Control: Corrigibility and domesticity; safe and unsafe agent architectures. Research Priorities Robust Artificial Intelligence, is concerned with the verification, validation, security and control of AI systems 1. Define business use case criticality and vulnerability 2. Select interpretability requirements in terms of explainability, transparency, and provability 3. Design and build models while performing business, performance, and acceptance trade-offs 4. Monitor ongoing model performance and governance Business Implications Verification Did I build the system right? • How to prove that a system satisfies certain desired formal properties? Validation Did I build the right system? • How to ensure that a system that meets its formal requirements does not have unwanted behaviors and consequences? Security How do I secure the system? • How to prevent intentional manipulation by unauthorized parties? Control How do I control the system? • How to enable meaningful human control over an AI system after it begins to operate? • Determine critical and vulnerable sectors (e.g., autonomous vehicles, healthcare systems, safety critical infrastructure, airspace) that require explicit regulations • Facilitate industry, research, and government discussions on Robust AI Regulatory Implications
- 36. PwC AI Lab| 36 Our Robust AI framework helps businesses design, build, and deploy AI systems that can be ‘trusted’ PwC’s Robust AI Framework AI Tradeoffs Monitoring of data for model training to ensure data does not skew model performance Determining artificial intelligence algorithm accuracy as required by business use case Ensuring algorithm decisions are explainable to end user in such a way the user trusts the predictions for the given use case Determining the appropriate scope and system requirements for an artificial intelligence application Identification of potential threats that may undermine or shift algorithm decision making Requiring artificial intelligence algorithms to function reliably and predictably
- 37. PwC AI Lab| 37 Explainable AI to improve customer experience Source: Gunning, DARPA I/2O, 2017
- 38. PwC AI Lab| 38 National AI Strategies USA Unmanned Aircraft Systems (UAS) (Oct 2017) Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights (May 2016) AI, Automation, and the Economy (Dec 2016) Preparing for the Future of Artificial Intelligence (Oct 2016) China Next generation AI Development Plan (July 2017) with key focus areas and key guarantee measures addressing the Science & Technology as well as regulations and competitive policies United Kingdom Growing the Artificial Intelligence Industry in the UK (October 2017): Recommendations to o Improve access to data o Maximize UK AI Research o Improve supply of skills o Support uptake of AI Germany Ethics Commission: Automated and Connected Driving (June 2017) Japan Artificial Intelligence Technology Strategy (March 2017) New Robot Strategy (February 2015)
- 39. PwC New Services| 39 Beneficial Artificial Intelligence • Economic Modeling of AI Adoption: Automation and AI impact - whom, when, and by how much; valuing knowledge and insights • Ethics research: Value alignment, AI rights, autonomous weapon systems ban and/or control • Wealth redistribution: Universal Basic Income and alternative policy assessment and experimentation Research Priorities Beneficial Artificial Intelligence, is concerned with maximizing the social benefit of Artificial Intelligence Business Implications Economic Issues How do we estimate benefits? • How do we calculate the economic impact of automation and AI? Social Issues How do we share benefits? • What social policies (e.g., universal basic income) to distribute the wealth generated by automation and AI? Legal Issues What rules & regulations do we need? • What laws do we need to pass to protect people, life, and property? Ethical Issues How do we ensure the AI is used for social good? • What values should autonomous systems have and who decides the values? • Liabilities and Laws for Autonomous systems: Autonomous car liability; drone air space regulations; road traffic rules • Policy Formulation: Taxation, education, social security, energy and transportation, competition, privacy, cyber , autonomous weapons etc. Regulatory Implications • Future of Work: Impact assessment of automation and AI; change management; training and re-skilling workforce; creation of new roles; community participation • Non-Profit Groups: Making the case for policy changes at the national (e.g., drone rules) and international levels (e.g., autonomous weapons ban)
- 40. PwC AI Lab| 40 Reskilling • Workforce reskilling • Digital fitness • University education Key Elements of AI Strategy 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
- 41. PwC AI Lab| 41 Augmented Intelligence
- 42. PwC AI Lab| 42 PwC’s Digital Services Thank you. © 2018 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. Rao Global AI Lead anand.s.rao@pwc.com @AnandSRao