AI is everywhere, but how can you ensure that your AI outputs are reliable and trustworthy? Scaling your AI projects, showing measurable success, and ensuring that the foundational data fueling the AI is clean, reliable, and trustworthy is an ongoing challenge for many businesses.  

While there are plenty of obstacles getting in the way of an AI initiative that can be trusted; insufficient, inadequate, and unreliable data tops the list. Other common factors that hinder a company’s journey to achieving Trusted AI include a lack of trust in the AI tools themselves and a lack of talent, who can make the most of these tools.

Join us for a discussion that addresses how businesses can overcome these challenges and learn about why a credible, reliable, and trusted data foundation is essential to maximizing your AI’s potential. In this session, we will discuss:

What role does foundational data play, and how can it help you ensure that your AI outputs can be trusted? 
• Making sure AI sources can be validated and AI is explainable
• Enforcing data quality, governance, and compliance
• The role of ecosystems for trusted AI

What does it take to create trust in AI tools? How can companies make trust foundational to their AI projects?
• Establishing human-in-the-loop accountability 
• Ensuring data security and privacy of personally identifiable information (PII) 

What role does AI Talent have in ensuring AI outputs can be trusted?

• Training and retaining AI talent on governance requirements for trusted AI

• How are specific industries upskilling current talent to create the workforce we need?

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