Responsible use of AI should both address the significant opportunities offered by AI and 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.
Anand Rao outlines the five pillars of responsible AI: bias and fairness; interpretability (including explainability); robustness and security; governance; and system ethics, morality, and legal. He presents a step-by-step approach to verify and validate AI (especially machine learning systems), focusing on key techniques and tools for testing, and in some cases refining, models to address the first three pillars. He then draws on case studies from financial services and healthcare to demonstrate the use of the responsible AI methodology to evaluate bias and fairness, build interpretable and explainable models, and make them robust and safe.
Anand uses the recently released FICO dataset for the XAI challenge to illustrate the techniques.
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’s 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 the US and Canada by Corinium. His recent paper, “A Strategist’s Guide to Artificial Intelligence,” won the 2017 Azbee Award for Best Paper. Anand holds an MSc in computer science from the Birla Institute of Technology and Science, 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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