When: Monday, November 9, 2020 from 10:00am to 12:00pm

Where: Virtually on Zoom ( Zoom details will be included with your registration confirmation)

Description:

With products such as Alexa, Google Home, Siri, and countless others, artificial intelligence-technologies have become a household mainstay. AI can enhance automation, improve forecasting, free up humans from tedious tasks, and revolutionize customer experiences. But, as with any innovation, there also come all of our Black Mirror-type fears, like “What if it goes wrong?”

With many examples of AI technologies not working as intended due to data biases and many other influences (hint: Google “Microsoft Tay”), it’s becoming critically important for organizations to better understand how to design better AI-enabled products and services.

In this interactive and challenging workshop, you’ll explore the relationship between data and predictive systems, and develop the curiosity and critical thinking skills to be more aware of how our biases can influence AI and machine learning systems. Participants will walk away with practical tools and frameworks that can be used to improve how AI is deployed to be more inclusive, accessible and reliable.

Learning Outcomes

• Understand the relationship between training data and predictive technologies
• Examine the wide range of biases that can influence AI systems
• Explore practices that teams and organizations can do to first become more aware of these biases and then design solutions that avoid them
• Grow your curiosity and critical thinking skills as it relates to data bias
• Gain experience using a range of tools and frameworks

Who Should Attend

• Product managers and directors
• Business leaders
• Startup founders and entrepreneurs working with AI
• HR professionals
• Data scientists and engineers
• User experience and customer experience designers

Agenda

• A rapid refresher on AI and machine learning
• How training data influences predictive systems
• Why AI ethics matters and what happens when things go wrong
• Interactive exercise: using the data ethics canvas to design an AI
• Introduction to the AI ethics guiding principles framework
• Breakout groups: how organizations can scale this knowledge and design better, more inclusive, and accessible AI solutions
• Discussion
• Q&A period