Overview
AI-based facial recognition technology is increasingly becoming available as a tool for businesses across a wide range of industries, from banking and financial services to airline travel, ecommerce and retail. While the pandemic accelerated the adoption of digital identity verification tools as a practical matter for fraud prevention, new AI-based facial recognition applications are entering the market and offering businesses opportunities to gain competitive advantage by better integration of customer service, improved customer engagement, enhanced safety and security, and streamlined business operations.
The development and use of AI-based facial recognition technology also gives rise to significant ethical, legal, regulatory, and commercial challenges stemming from the well-documented possibility of racial and gender biases and other risks inherent in the use of this technology. Please join us for a practical discussion of some of the benefits and risks of adopting facial recognition technology, and the steps that organizations should consider in order to manage these risks. Topics include:
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Understanding the AI systems underlying facial recognition technology, and why the use of AI raises novel legal, ethical and governance issues
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A high-level review of the current market for facial recognition technology applications and its future potential, including digital identity verification tools and other mainstream uses
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A look at the evolving regulatory landscape in Canada
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An analysis of the key risks that the use of AI facial recognition technology presents for businesses, including human rights, privacy, data security, and reputational risks
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Risk management strategies and the importance of establishing an ethical data governance framework for the use of AI technologies
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Key considerations from both the customer and vendor perspective for the drafting and negotiation of commercial agreements for the development, deployment and commercialization of AI-based facial recognition technology, including terms and strategies to address complex issues such as ownership of models, learning algorithms, and data use rights