Even without intentionally prejudice data or development practices, AI can produce inequitable results. How can organizations ensure they are mitigating bias at all levels and reducing the risk of reputational, societal and regulatory harm?

AI has the potential to unlock significant long-term value but trust needs to be felt both by the company deploying the AI and the customers that will experience it and its outcomes. This discussion will explore the success and failures organizations have faced with:

• Effectively building trust and conveying the net benefits for all parties
• Human circuit breaks as a safety mechanism
• Leveraging trust to create more desirable business outcomes
• The definition of trust in the context of AI – is it in the development process, the outcome or both?

• How to test, evaluate and analyze AI systems
• Adopting comprehensive test and evaluation approaches
• Which protocols can be applied and where new approaches are required

All systems fail at some point, no matter how much time and rigor are put into their design and development. AI is not immune, susceptible to attacks, exploitation and unexpected failures. This session will be broken into two presentations to explore:

• Top tips for designing, building and ensuring robustness and resilience in AI
• Improving the robustness of AI components and systems
• Designing for security challenges and strategies for risk mitigation

How much does it matter than systems are uniquely tailored to your business case? Are you sure you can fully explain the AI models you are using, and do you even need to? There are clear pros and cons for both developing models internally and buying third party but whichever route you choose, have you considered the risks and if so, how are you mitigating them?

An opportunity to hear first-hand insight and ask questions on how regulation is taking shape, what to expect and what you can do to prepare now. 

Get ahead of the curve with an understanding of how different businesses are proactively preparing as the US and EU start to align on future AI regulation. This panel will explore how regulation is likely to take shape and what organizations should be doing now to ensure they are proactively prepared.

Two-way communication is imperative for an effective business framework – and AI development is no different. But with disparate understanding, expertise and focus across different teams and sectors, how can organizations establish effective communication? A panel of experts will establish what works, what doesn’t and how to get there. 

AI governance frameworks could help organizations learn, govern, monitor, and mature AI adoption and scale. While there is no one-size-fits-all approach, organizations can consider adopting processes to mitigate risk. This session will explore:

• What an effective AI governance and risk management framework looks like in practice
• The core principles that can be operationalized
• Implementation of a functional framework irrespective of available resources and organization size
• The most vital aspects of a framework and how to tailor them based on need
• Generating maximum additional value as a result