Representation for greater AI benefits

AI’s potential to benefit society will only be realized if it is representative of all humanity. Join this panel to explore how embracing equity at every stage of analytics can deliver bigger benefits.

Discussion triggers

  1. Why is equity and inclusion in AI important?
  2. How can data collection respect principles of inclusion and equity?
  3. How feasible is representation in model design, iteration, development and testing?
  4. What are the characteristics of an inclusive AI infrastructure?
  5. How effective are deployment and monitoring of community impact?
  6. How are universities rising to the challenge of assuring representative 

Past panel discussions on diversity, inclusion and representation


How does diversity power collaboration?

Innovation needs more than creativity. Effective collaboration is at the heart of turning ideas into reality. Fluency in diverse thinking and approaches is increasingly recognized as a super-power.


Diverse Data Science Talent

Data science is so much more than modelling. Increasingly, collaboration across disciplines is also key to unlocking value from insights. Research suggests diverse teams learn to collaborate more effectively.


Designing better AI centers of excellence

Artificial intelligence (AI) centers typically include practitioners who can advise on AI projects to ensure both quality and value.


Can conversational AI supercharge smaller teams?

Augmenting human creativity and endeavors with AI seems more pressing for smaller teams. And yet adoption rates in the midmarket lag behind enterprises and startups.

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