[AISWorld] Explainable AI @ UT Austin's Global Analytics Conference, Nov. 11-12, 2021

[Tanriverdi, Huseyin]

UT and the McCombs School of Business’ Center for Analytics & Transformative Technologies (CATT) would like to invite you to its 2021 Global Analytics Conference, being held online on November 11 (afternoon) and 12 (morning).  This year’s theme is “Explainable AI.”   Please share the following information with your colleagues who would find this of interest.

The registration fee will be waived for this year’s online conference and attendance will be completely free of charge.  Be sure to reserve your spot by registering soon, as attendance is limited.

To view program information and to register, please visit:
https://bit.ly/3pnM5Hz

As artificial intelligence and machine learning techniques are evolving and becoming ever more sophisticated, their value to businesses are also increasing.  But this sophistication inevitably brings some opacity—and that opacity can be a barrier to more widespread adoption.  Business leaders and their data science organizations require solutions that are interpretable, transparent, fair, and defensible—in short, they need solutions that are explainable.  To overcome the stigma of the “black box,” explanations are needed for a wide and diverse set of audiences, including management and senior leadership, customers and clients, regulators, and other internal or external stakeholders.  The implications are sweeping, including in areas like risk management, ethics, compliance, reliability, and customer relationship management.  This conference will feature leading experts from both academia and business to share current best practices and research on effective, interpretable, and explainable AI.

We have a terrific lineup of great speakers over the two half-day sessions:

PROGRAM AGENDA


November 11, 2021 (afternoon session)



12:45pm       Introductory Remarks

·                    Lillian Mills, PhD (Dean, McCombs School of Business, UT Austin) 



1:00pm       Keynote I:  Explainability and More:  What is Needed to Get a Model Into Production

·                   Charles Elkan, PhD (Professor of Computer Science, UCSD & Former Managing Director, Goldman Sachs) 

·        Introduced by Joydeep Ghosh, PhD (Professor of Electrical and Computer Engineering, UT Austin)



1:50pm       Panel I:  Explainable vs. Ethical AI—Just Semantics?

·                   Moderator:  TBD (UT Austin)

Panelists:

·                   Polo Chau, PhD (Assoc. Prof., School of Computational Science & Engineering, Georgia Tech)

·                   Alice Xiang, JD (Sr. Research Scientist & Head of AI Ethics Office, Sony Group)

·                   Deepayan Chakrabarti, PhD (Asst. Professor, Dept. of IROM, UT Austin)



2:50pm       BREAK



3:00pm       Presentation/Talk I:  Responsible AI in Industry

·                   Krishnaram Kenthapadi, PhD (Principal Scientist, Amazon AWS AI)



3:50pm       Panel II:  Adopting AI:  Industry Challenges and the Role of XAI

·                   Moderator:  Junfeng Jiao, PhD (Director, Urban Information Lab, School of Architecture & Director, Good Systems, UT Austin)

Speakers:

·                   Michael Shepherd (Distinguished Engineer, Dell Technologies)

·                   James Guszcza, PhD (Chief Data Scientist, Deloitte LLP & Research Affiliate, CASBS, Stanford University)

·                   Hima Lakkaraju, PhD (Asst. Prof., HBS and Dept. of CS, Harvard University)



5:00pm       Presentation/Talk II:  The Role of Explainable AI when “Data is the New Programming Language”

·                   Mark Johnson, PhD (Chief AI Scientist, Oracle Corp.)



November 12, 2020 (morning session)



8:45am       Introductory Remarks



9:00am       Keynote II:  Scoring Systems:  At the Extreme of Interpretable Machine Learning

·                   Cynthia Rudin, PhD (Professor of CS, Duke University & Principal Investigator, Interpretable Machine Lab) 

·        Introduced by Kumar Muthuraman, PhD (Professor of IROM & Faculty Director for CATT, UT Austin)



9:50am       Panel III:  XAI Solutions—Different Approaches to Explainability

·                   Moderator:  Maria DeArteaga, PhD (Asst. Prof., Dept. of IROM, UT Austin)

Panelists:

·                   Scott Lundberg, PhD (Senior Researcher, Microsoft Research)

·                   Jette Henderson, PhD (Senior Machine Learning Scientist, CognitiveScale, Inc.)

·                   Zachary Lipton, PhD (Asst. Prof., Dept. of Operations Research & Machine Learning, Carnegie Mellon University)



10:50am       BREAK



11:00am       Presentation/Talk III:  Explainable AI for Intelligent Financial Services:  Examples and Challenges

·                   Daniele Magazzeni, PhD (AI Research Director & Head of the Explainable AI Center of Excellence, JP Morgan)



11:50am       Panel IV:  Explanations, but for Whom?

·                   Moderator:  Raymond Mooney, PhD (Professor of CS & Director of UT Artificial Intelligence Lab)

Panelists:

·                   Christoforos Anagnostopoulos, PhD (Senior Principal Data Scientist, McKinsey & Co.)

·                   Nazneen Rajani, PhD (Research Scientist, Salesforce Research)

·                   Sanmi Koyejo, PhD (Assoc. Professor, Dept. of CS, University of Illinois at Urbana-Champaign)



12:50pm     Closing Remarks

·                   Susan Broniarczyk, PhD (Associate Dean for Research & Professor of Marketing, UT Austin)



1:00pm       Formal Conference Concludes



This year’s conference has been made possible with the generous support of the McCombs School of Business, Dell Technologies, and the Good Systems Initiative—A UT Grand Challenge.


CATT GLOBAL ANALYTICS CONFERENCE 2021 (XAI) PROGRAM COMMITTEE
__________________________________________________
Center for Analytics and Transformative Technologies
McCombs School of Business
The University of Texas at Austin
Office: CBA 6.316 | (512) 232-2735
Mail:  2110 Speedway, Stop B6600
Austin, TX  78712-1276