Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY

Executive Briefing: Responsible AI - Approach & Case Studies in building Fair, Interpretable, Safe AI

Anand Rao (PwC)
1:50pm2:30pm Wednesday, April 17, 2019
Secondary topics:  Ethics, Privacy, and Security, Financial Services, Health and Medicine, Reliability and Safety

Who is this presentation for?

Chief Risk Officers, Chief Analytics Officers or AI Leads

Level

Intermediate

Prerequisite knowledge

- Experience in building AI-based proof-of-concepts or pilots (specifically using machine learning or deep learning models in natural language text or other unstructured data)

What you'll learn

- Learn key risks of AI and the implications of these risks to business - Learn best practices in five key areas of Responsible AI - Learn techniques and open source tools available to address the risks - Learn step-by-step methodology for scoping, building, testing, and continuously monitoring machine learning systems

Description

Responsible use of AI should address not only the significant opportunities offered by AI, but must also mitigate some of the major risks of AI. These risks span a broad range of risk categories including performance, control, security, economic, ethics, and society. Recent research in academic circles highlight a number of key issues related to fairness, accountability, and transparency (FAT).

However, these academic studies fall short of providing step-by-step guidance to practitioners in business who are primarily concerned about these issues and have to demonstrate fairness, accountability, explainability and other qualities to regulators or to customers to gain their trust.

In this talk we describe the five pillars of responsible AI: (a) Bias and Fairness; (b) (Interpretability (including explainability); © Robustness and Security; (d) Governance; (e) System Ethics, Morality and Legal. We then present a step-by-step approach to verify and validate AI (especially Machine Learning systems). We focus on key techniques and tools for testing, and in some cases refining, models to address the first three pillars. Next we take two case studies one in financial services and the other in healthcare to demonstrate the use of the Responsible AI methodology to evaluate bias and fairness, build interpretable and explainable models, and to make them robust and safe.

The recently released FICO dataset for the XAI challenge will be used as an example to illustrate the techniques.

Photo of Anand Rao

Anand Rao

PwC

Anand Rao is a partner in PwC’s Advisory practice and is the Global AI Lead and the competency lead for the Analytics practice in US. He leads the design and deployment of artificial intelligence and other advanced analytical techniques and decision support systems for clients, including natural language processing, text mining, social listening, speech and video analytics, machine learning, deep learning, intelligent agents, and simulation. Anand is responsible for research and commercial relationships with academic institutions and startups.

Previously, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute; program director for the Center of Intelligent Decision Systems at the University of Melbourne, Australia; and a student fellow at IBM’s T.J. Watson Research Center. He has held a number of board positions at startups.

Anand has coedited four books and published over 50 papers in refereed journals and conferences. He was awarded the most influential paper award for the decade in 2007 from Autonomous Agents and Multi-Agent Systems (AAMAS) for his work on intelligent agents. He is a frequent speaker on AI, behavioral economics, autonomous cars and their impact, analytics, and technology topics in academic and trade forums.

Anand was recently selected as one of the Top 100 innovators of Data and Analytics and also in the Top 50 Data and Analytics professionals in US and Canada by Corinium. His recent paper on Strategist’s Guide to AI won the 2017 Azbee Award for Best Paper.

Anand holds an MSc in computer science from Birla Institute of Technology and Science in India, a PhD in artificial intelligence from the University of Sydney, where he was awarded the university postgraduate research award, and an MBA with distinction from Melbourne Business School.

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