Four Key Ingredients in a Responsible Enterprise AI Journey

On February 28th, fifteen CDO’s, Data Governance, Digital Marketing, Innovation and Risk Management and other executives from some of the largest US and Canadian organizations, including Scotiabank, RBC, TD, BMO, Manulife Great West Life, TELUS, General Motors and other, joined our private breakfast event in Shangri-La Toronto to discuss Responsible AI - Opportunities and Challenges for Businesses in Regulated Industries.

Artificial Intelligence promises to turn your data into increased revenues and operational efficiencies by processing often sensitive customer data that is regulated by GDPR, PIPEDA and other soon-to-be effective and increasingly stringent privacy regulations.

Geoffrey Hunter talked about business opportunities with examples of successful AI initiatives. Stephanie Davis presented AI Governance framework and emerging “Top Tips” to help organizations procure and operate Artificial Intelligence-based products.

Major takeaways from Geoffrey’s talk:

JPMorgan Chase & Co successfully implemented a Contract Intelligence (COIN) technology that interprets commercial-loan agreements:

  • Executes 360,000 hours of manual labor each year

  • Reduces human error and, to automate mundane tasks granting access to software systems and responding to IT requests an AI-powered chat-bot system:
  • Handles 1.7 million requests in 2017 or an equivalent work of ~140 people

According to JPMorgan COO Matt Zames - “We’re starting to see the real fruits of our labor. This is not pie-in-the-sky stuff.”

Responsible AI Journey:

  1. Establishing common understanding and awareness of AI’s potential value increases awareness of AI value and makes first operationalization steps smoother.
  2. Point Solutions - investing in capabilities to solve narrow business problems with specific outcomes creates a better enterprise AI Journey with greater internal support.
  3. Differential privacy and Responsible AI frameworks that TribalScale uses address five key requirements: Data Security, Customer Privacy, AI Bias, Human Oversight, Human-centered Design.
  4. On-Device AI Model is a promising approach for addressing data privacy and ownership concerns by separating Training AI Model and production deployment of AI Model directly onto the handheld devices and eliminating the need for transferring PII data.

Key takeaways from Stephanie David talk about AI Governance:

  • Mature Data Governance framework helps shorten time-to-value stages and mitigating risks of AI technologies by ensuring consistent and transparent management, maintenance, and use of organizational data.
  • An effective Governance model includes AI, strategy, people, process, technology, and data must be working cohesively. The well-known PEOPLE, PROCESSES, TECHNOLOGY framework is maturing into STRATEGY, PEOPLE, PROCESS, TECHNOLOGY, AND DATA.
  • Best AI Governance should are addressing six governance layers: Information Model, Data Sources, Data Quality, Privacy & Security, Ethics & Sharing, Regulation & Compliance

Key slides of Geoffrey’s presentation are attached to this email. Please feel free to contact him directly if you have any questions or need The AI Journey… and its Challenges; Responsible AI; Differential Privacy; and On-Device AI Model frameworks.

Tips from Stephanie:

  1. Start with an end goal in mind and focus on with business problems
  2. Have good communication and change management in place
  3. Validating AI solutions against ethical and privacy principles
  4. Treat data as an asset
  5. Collect data with a goal in mind

Responsible AI and Governance of AI speeches were followed by a fireside chat moderated by Ivan Tsarynny, founder & CEO of Feroot Security. AJ Khan, CCSK a cybersecurity practice lead and the founder of Cloud GRC joined Geoffrey Hunter and Stephanie Davis to discuss the newest AI use-case trends, streamlining internal stakeholder collaboration, and enhancing AI compliance & security.

About speakers:

Geoffrey Hunter Ph.D., a recognized AI expert, speaker, Data Scientist and the head of AI Strategy at TribalScale. Geoffrey leads the Artificial Intelligence and Machine Learning practice at TribalScale an innovation firm that creates digital products for web, mobile, and emerging platforms. Geoffrey served as a subject matter expert and thought the leader in data science, cognitive technologies, and robotic process automation at Deloitte and at Widgets and Digits: Data Science Consultants. Prior to consulting, he was a cancer researcher at the Ontario Institute for Cancer Research where he used machine learning to improve the prognosis of cancer patients. Geoffrey holds a Ph.D. and MS in Mathematical Physiology from the University of Utah and a BMath in Applied Math from the University of Waterloo.

Stephanie Davis, a Senior Cyber Risk Consultant from Deloitte and a frequent speaker on information risk strategies talk about new AI Opportunities for Businesses in Regulated Industries, operationalizing AI data sharing with third-party technologies. Stephanie has spent the last five years helping clients solve their privacy, data protection, and governance challenges. With a background in knowledge management and information science, Stephanie is using those skills as she builds Deloitte’s AI Governance practice. An active proponent of enterprise-wide change and innovation through the use of data-driven decision-making, Stephanie helps organizations gain value from their data and execute on strategy using effective governance mechanisms.

Picture of Lori Smith
Lori Smith
Lori is marketing lead at Feroot Privacy

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