Machine learning and artificial intelligence algorithms hold great promise for many business applications. As these systems become more widely adopted, business and technical leaders require trusted ML/AI systems to exhibit increased transparency, fairness, and interpretability.
The implications for organizations are sweeping, including for: risk management, ethics, compliance, reliability, and customer relationship management.
This conference will explore best practices and current research on effective, interpretable, and explainable AI.
November 11 – 12, 2021
Online Event | Confirmation Sent Upon Registration
Want to stay up to date with our events?
Join our mailing list for upcoming events and announcements.
November 11 – 12, 2021
This is a virtual conference.
All registrants will receive a program following the event.
Follow Us
© 2021 McCombs School of Business, The University of Texas at Austin
Agenda
Thursday | November 11, 2021
12:45 PM
1:00 PM
Morning Keynote Presentation/Talk
“Explainability and More: What is Needed to Get a Model Into Production”
Charles Elkan
Professor of Computer Science, University of California San Diego
Former Managing Director, Goldman Sachs & Co.
Introduced by:
Joydeep Ghosh
Professor of Electrical and Computer Engineering, University of Texas at Austin
Moderator:
Michael Sury
Managing Director, Center for Analytics; and Faculty, Dept. of Finance, UT Austin
Speakers:
Alex London
Professor of Ethics and Philosophy, Carnegie Mellon University
Director of the Center for Ethics and Policy
Hima Lakkaraju
Asst. Professor, Harvard Business School and Dept. of CS, Harvard University
Polo Chau
Assoc. Professor, School of Computational Science & Engineering, Georgia Tech
Alice Xiang
Sr. Research Scientist and Head of AI Ethics Office, Sony Group
- Is one a subset of the other?
- Does an ethical system need to be explainable?
- Does transparency increase the perception of ethical integrity?
- Is explainability just a “red herring”?
Panel – Explainable vs. Ethical AI: Just Semantics?
1:50 PM
“Responsible AI in Industry”
Krishnaram Kenthapadi
Principal Scientist, Amazon AWS AI
Presentation/Talk
3:00 PM
Break
2:50 PM
Panel – Adopting AI: Industry Challenges and the Role of XAI
- How to deliver XAI in multi-layered corporate organizations
- Mismatch of industrial expectations with statistical reality
- Is attaining explanations realistic for complex systems?
- Navigating corporate reporting systems
3:50 PM
Moderator:
Junfeng Jiao
Director, Urban Information Lab in UT’s School of Architecture; Chairman, Good Systems
Speakers:
Michael Shepherd
Distinguished Engineer, AI Research, Dell Technologies
Anand Rao
Principal and Global AI Lead, Pricewaterhouse Coopers
James Guszcza
Research Affiliate, Center for Advanced Study in the Behavioral Sciences, Stanford University
Chief Data Scientist, Deloitte LLP
Break
4:50 PM
Presentation/Talk
5:00 PM
The Role of Explainable AI when “Data is the New Programming Language”
Mark Johnson
Chief AI Scientist, Oracle Corp.
Friday | November 12, 2021
Welcoming Remarks Day 2
8:45 AM
9:00 AM
Morning Keynote Presentation/Talk
“Scoring Systems: At the Extreme of Interpretable Machine Learning”
Cynthia Rudin
Professor of Computer Science, Duke University
Principal Investigator, Interpretable Machine Lab
9:50 AM
Panel – XAI Solutions: Different Approaches to Explainability
- Different ways to explain models: Rules? Prototypes? Counterfactuals? SHAP values? etc.
- Are there clearly superior explanations that resonate with human audiences?
- What happens when explanations contradict domain knowledge?
Moderator:
Maria De-Arteaga
Assistant Professor, Information, Risk, and Operations Management
McCombs School of Business
Speakers:
Scott Lundberg
Senior Researcher, Microsoft Corp.
Jette Henderson
Senior Machine Learning Scientist, CognitiveScale, Inc.
Zachary Lipton
Asst. Prof., Dept. of Operations Research & Machine Learning, Carnegie Mellon University
Presentation/Talk
"Explainable AI for Intelligent Financial Services: Examples and Challenges"
Daniele Magazzeni
AI Research Director and Head of the Explainable AI Center of Excellence, JP Morgan
11:00 AM
Break
10:50 AM
11:50 AM
- Crafting XAI for different stakeholders with different agendas
- Multiple Explanations vs “One Size Fits All”
- Methods of delivery
Panel – Explanations, But for Whom?
Moderator:
Raymond Mooney
Professor of Computer Science and Director, UT AI Lab
Speakers:
Christoforos Anagnostopoulos
Senior Principal Data Scientist, McKinsey & Co.
Nazneen Rajani
Research Scientist, Salesforce Research
Sanmi Koyejo
Assoc. Professor, Dept. of Computer Science, University of Illinois at Urbana-Champaign
Closing Remarks
12:50 PM
McCombs School of Business,
The University of Texas at Austin
Professor of Computer Science, University of California, San Diego
Former Managing Director, Goldman Sachs & Co.
Chief Data Scientist, Deloitte LLP
Research Affiliate, Center for Advanced Study in the Behavioral Sciences, Stanford University
Associate Dean for Research and Professor of Marketing, UT Austin
CATT 2021 Global Analytics Summit on
EXPLAINABLE AI
Principal Investigator, Interpretable Machine Lab
Hosts
Susan M. Broniarczyk is Associate Dean for Research, Susie and John L. Adams Endowed Chair in Business, and a Professor in the Marketing Department.
Her research examines consumer behavior and decision making with a focus on brand and product management, product recommendations and advice, and gift-giving.
The Society for Consumer Psychology awarded her its first Early Career Contribution Award and the American Marketing Association awarded her dissertation on branding the John A. Howard award. Her research on product assortment won the O’Dell Award for its long-term significance to marketing theory and practice in the Journal of Marketing Research.
Her research has appeared in leading academic journals including the Journal of Consumer Research, Journal of Marketing Research, Journal of Consumer Psychology, Journal of Personality and Social Psychology, Journal of Public Policy & Marketing, Journal of Retailing, Journal of Academy of Marketing Science and Organizational Behavior and Human Decision Processes.
Her research has also been featured in the media including Time Magazine, Business Week, and U.S. News and World Report. She is former President of the Society for Consumer Psychology and has been active in the Association for Consumer Research serving on its advisory board, as Treasurer, and ACR conference co-chair.
Michael Sury is the managing director of the Center for Analytics and Transformative Technology, and he is an award-winning professor who has taught at both the undergraduate and graduate levels for over 15 years. Previously, Sury began his career working in technology in 1986, designing intelligent systems architectures in the AI Group at MCC, the noted R&D consortium formed by tech heavyweights including Intel, GE, and Microsoft. Sury later worked for Lockheed Missiles & Space Co. on classified projects for the Department of Defense’s Strategic Defense Initiative; and IBM, where he taught and implemented statistical process control and real-time analytics for manufacturing engineering across the IBM PC production line. After graduate school at the University of Chicago, Sury was recruited by Goldman Sachs & Co., and he served as a vice president in the firm’s equities and investment management divisions. He later led one of the nation’s top-ranked wealth management and institutional broker-dealer trading firms before selling his stake and entering academia. He has taught at the University of California and Santa Clara University, and he is now on the faculty of the Department of Finance at The University of Texas’ McCombs School of Business. At UT, he serves as the program director of the financial analytics track within the Master of Science in Business Analytics program, and as a member of the McCombs School of Business Analytics Task Force. He has delivered more than 80 conference speeches in the U.S. and internationally and has appeared on major broadcast networks and in a variety of news publications for his insights and commentary on financial analytics and the capital markets.
Faculty Director, Center for Analytics and Transformative Technologies; Professor of Information, Risk, and Operations Management McCombs School of Business, The University of Texas at Austin
Kumar Muthuraman is the H. Timothy (Tim) Harkins Centennial Professor in the Department of Information, Risk, and Operations Management and the Department of Finance. He received a Ph.D. from Stanford University. Muthuraman’s research focuses on decision making under uncertainty. Application areas of interest to him are quantitative finance, operations management, and health care.
Joydeep Ghosh is the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at The University of Texas at Austin.He joined the UTAustin faculty in 1988 after being educated at IITKanpur (B. Tech '83) and the University of Southern California (Ph.D. ’88). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab) and a fellow of the Institute of Electrical and Electronics Engineers. Ghosh has taught graduate courses on data mining and web analytics to UT students and members of industry for more than a decade. He was voted Best Professor in the Software Engineering Executive Education Program at UT. Ghosh's research interests lie primarily in data mining and web mining, predictive modeling/predictive analytics, machine learning approaches such as adaptive multi-learner systems, and their applications to a wide variety of complex real-world problems.
Professor, Department of Electrical and Computer Engineering, University of Texas at Austin
Keynotes
Speakers/Panelists
Charles Elkan served as the managing director and the global head of machine learning at Goldman Sachs in New York and is a professor of computer science at the University of California, San Diego. Previously, he was the first Amazon fellow and the leader of Amazon's central machine learning team in the U.S. He had previously been a visiting associate professor at Harvard University. Elkan's research has been mainly in machine learning, data science, and computational biology. In particular, the MEME algorithm that he developed with doctoral students has been used in more than 4,000 published research projects in biology. He is fortunate to have had inspiring undergraduate and graduate students who have become faculty members at institutions including Columbia University, Stanford University, and the University of Washington, or who have become leaders at Google and other major companies.
Cynthia Rudin is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics and bioinformatics at Duke University, and she directs the Interpretable Machine Learning Lab. Previously, Rudin held positions at the Massachusetts Institute of Technology, Columbia University, and New York University. She holds an undergraduate degree from the University at Buffalo and a Ph.D. from Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and she was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.
Sponsors
Susan Broniarczyk
Associate Dean for Research and Professor of Marketing, UT Austin
Kumar Muthuraman
Faculty Director, Center for Analytics & Transformative Technologies
Professor, Department of Information, Risk & Operations Management, UT Austin
MICHAEL SURY
Managing Director
Center for Analytics and Transformative Technologies
Director of the Center or Ethics and Policy
