Responsible Innovation

Data is a powerful ally for innovators. Join this discussion to explore why designing data-driven systems responsibly requires asking not only what can be done, but also how options should be navigated. 

Discussion triggers

  1. What does it mean for AI to be biased? How does AI become biased?
  2. What does it mean for AI to be fair and trustworthy?
  3. What are some of the ways in which organizations struggle to ethically implement AI?
  4. How are organizations handling and addressing concerns around bias and unfairness?
  5. What can organizations do to ensure they are innovating responsibly?

Further reading

Who is responsible when AI is irresponsible?

Ethics in AI: 2 sides of the same coin

The strange bedfellows of AI and ethics

Interpretability vs. explainability: The black box of machine learning

Next generation of responsible AI innovators tackle real-world challenges with AI4ALL and SAS

Exploring innovative modeling designs built to battle bias

4 architecture takeaways from my MSc in AI