Fairness, Trustworthiness, and Transparency for AI Systems (panel)

Rapid technical advances have led to AI capabilities that were unimaginable only ten years ago.   With these successes in hand, attention is shifting to how AI technologies can and should be deployed in real-world settings: while good performance is essential, qualities such as fairness, trustworthiness, and transparency are becoming increasingly critical for technology acceptance. This panel will explore factors motivating these usability requirements and discuss current research aimed at satisfying them.

• Making AI trustworthy - Challenges and technology readiness
• Making AI trustworthy why robustness and fairness matter
• Preventing breakdown and brittleness of models
• Safeguards that prevent abuse and malicious behavior of AI models
• Addressing adversarial perturbations, examples and attacks
• Explainability, causality, and social good
• Deep learning models
• Understanding how the changing cognitive capabilities for AIs will lead to new ways for human-machine collaboration;

Moderator: Karen Myers, PhD, Lab Director, SRI International's Artificial Intelligence Center; Panelists: Roberta Stempfley, Director CERT Division, Software Engineering Institute, Carnegie Mellon University; Mark Stefik, Research Fellow, Lead, Explainable AI, PARC; Victor S.Y. Lo, PhD, Head of Data Science and Artificial Intelligence, Workplace Solutions, Fidelity Investments; Pin-Yu Chen, PhD, Chief Scientist, RPI-IBM AI Research Center, Research Staff Member, Trusted AI Group, IBM Thomas J. Watson Research Center