making artificial intelligence trustworthy seminar


Wednesday, October 23

7:30 am Registration Open

8:00 Continental Breakfast

8:55 – 12:05 pm AI World Executive Summit

12:05 Enjoy Lunch on Your Own

1:15 Making AI Trustworthy

Pin-Yu Chen, PhD, Chief Scientist, RPI-IBM AI Research Center, Research Staff Member, Trusted AI Group, IBM Thomas J. Watson Research Center

This talk will include a holistic overview of IBM Research's portfolio toward trustworthy AI, including the dimensions of fairness, explainability, adversarial robustness and transparency. What you will learn in this presentation:

  • Motivations and techniques for making AI models fair, explainable, robust, and transparent
  • Advanced research for trustworthy AI
  • Opensource libraries for trustworthy AI

1:55 Trusted AI for Advancing Science and Innovation

Payel Das, PhD, Research Staff Scientist and Manager, Deep Learning, AI Learning Department, IBM Thomas J. Watson Research Center; Adjunct Associate Professor, APAM Department, Columbia University

Scientific discovery is one of primary factors underlying advancement of human race. However, traditional discovery process is slow compared to the growing need of new inventions, for example, new antibiotic discovery or design of next-generation energy material. Data-driven approaches such as machine learning and, especially, deep learning have achieved remarkable performance in many domains including computer vision, speech recognition, audio synthesis, and natural language processing and generation in recent years. Those methods have also infiltrated other fields of science including physics, chemistry, and medicine. Despite these successes and the potential to make huge societal impact, machine learning models are still at infancy in terms of driving and transforming scientific discovery.  In this talk, I will talk about a closed-loop paradigm to accelerate scientific discovery, which can seamlessly integrate machine learning, physics-based simulations, and wet lab experiments and enable new hypothesis and/or artefact generation and validation thereof. Development and use of deep generative models and reinforcement learning-based methods for designing novel peptides and materials with desired functionality will be discussed. Finally, I will discuss the importance of adding crucial aspects, e.g. creativity, robustness, and interpretability, to the machine learning models in order to enable and add value to AI-driven discovery.

2:35 Refreshment Break

2:55 PANEL: Trusted AI

Moderator: Prasanna Sattigeri, Research, MIT-IBM Watson AI Lab, Thomas J. Watson Research Center

Panelists: David Sontag, PhD, Professor, Institute for Medical Engineering and Science (IMES); Principal Investigator, Computer Science and Artificial Intelligence Laboratory (CSAIL)

Himabindu Lakkaraju, PhD, Harvard University

Aleksander Mądry, PhD, Associate Professor, Computer Science, MIT, Member of CSAIL

Elisa Celis, PhD, Assistant Professor, Statistics & Data Science, Yale University

  • Fairness: Are our models fair? Unwanted bias and algorithmic fairness.
  • Explainability: Are the model decisions understandable? Interpretability and causal inference.
  • Robustness: Can our models be exploited? Safeguards that prevent abuse and malicious behavior of AI models.
  • Assurance: How to build accountability into our AI systems? Transparent reporting mechanisms on how AI models operate.

4:10 Session Break

4:20 Plenary Keynote Panel  

5:00 Grand Opening Reception in the Expo

6:30 Attendee Roundtable Discussions & Meetup Groups

7:30 Close of Day