Artificial Intelligence (AI), Ethics and the Law for Financial Services 2024

Andriana Cvetkovik, AVP, Global Privacy and Data Ethics, Sun Life
Diana Drappel, Assistant General Counsel, Emerging Technologies and Data, RBC

Dan Adamson, Co-Founder and Interim CEO, AutoAlign AI; Co-Founder, Armilla Assurance/AI, and PointChain Technology; Advisor,  Exiger
Dave Keirstead, VP, Chief Data & Analytics Officer Canada, Sun Life
Tamara O’Connell, BEng, JD, Patent Agent, Patent Portfolio Manager, University of Toronto – Innovations & Partnerships Office

Learn how AI technology is used in financial services and how to recognize and address the complexity of the inherent risks. Faculty will focus on the mechanics and distinctions of each technology, typical use cases, opportunities, and challenges in the Canadian financial industry.

• Overview of current AI technology and how it is being used in the financial industry
• The anatomy, mechanics and terminology of machine learning (ML), predictive analytics, and generative AI (GenAI)
• How each technology works, how the models are built and the key differences between ML, AI and GenAI
• A critical analysis of the application and common use cases within the financial industry
• Emerging technology: where are things going?

Carole Piovesan, Co‑Founder, Partner, INQ Law
Jasmine Samra, Counsel, Gowling WLG LLP

Ms. Piovesan and Ms. Samra will examine the latest developments in AI‑related law, regulation and policies and how financial institutions are currently navigating them. They will also outline current and emerging standards and guidelines for financial services, nationally and globally, including:

• The current state of the law in Canada and how it impacts financial services
• Emerging laws, policy developments and regulatory guidance in Canada and globally
• How the EU AI Act, the world’s first comprehensive AI law, will impact AI regulation globally and in Canada
• How financial institutions in other jurisdictions are navigating increased scrutiny in this area, despite lagging legal and prescriptive regulatory guidance
• The application of existing legal frameworks and strategies

Andriana Cvetkovik, AVP, Global Privacy and Data Ethics, Sun Life
Suzanne Morin, VP, Enterprise Conduct, Data Ethics & Chief Privacy Officer, Sun Life
Andres Rojas, Director, Applied AI Projects, Vector Institute 

This panel will delve into key AI‑related ethical and privacy principles contributing to the changing regulatory environment and what organizations can do to augment their privacy management programs with AI governance. They will also outline techniques and strategies to develop frameworks for augmenting responsible AI programs and associated policies, and the processes required to align with emerging regulations and expectations.

• How to identify and address privacy and ethical considerations, and to evaluate and prioritize legal and organizational risk
• What are the GenAI‑specific risks?
• How to create adaptive AI‑responsible governance polices and frameworks in the context of a changing regulatory environment
• Techniques for embedding an “effective challenge approach” within the governance framework to enhance the broader organizational risk culture
• How to build expertise to support human oversight and risk‑based decision making

Olalekan (Wole) Akinremi, Partner, Deeth Williams Wall LLP
Brian Chau, Partner, Norton Rose Fulbright Canada LLP
Diana Drappel, Assistant General Counsel, Emerging Technologies and Data, RBC 

Gain expert insight into the risks specific to the use of third parties for outsourcing and model development, including the sharing of large amounts of data, third‑party methodology and underlying assumptions, and whether they comply with the privacy and ethical principles defined by the financial institution.

• Understanding third party risk – why is it so complicated?
• Addressing transparency, explainability, and IP concerns
• Identifying and managing emerging litigation risk
• Securing data confidentiality, compliance and operational risk issues
• Open source GenAI – overview of the inherent risks and challenges, emerging trends and expectations, as well as strategies for navigating them in areas such as bias, output opaqueness, privacy and cybersecurity