In this enlightening episode, Dr. Julia Stoyanovich delves into the world of responsible AI, exploring the ethical, societal, and technological implications of AI systems. She underscores the importance of global regulations, human-centric decision-making, and the proactive management of biases and risks associated with AI deployment. Through her expert lens, Dr. Stoyanovich advocates for a future where AI is not only innovative but also equitable, transparent, and aligned with human values.

Julia is an Institute Associate Professor at NYU in both the Tandon School of Engineering, and the Center for Data Science.  In addition she is Director of the Center for Responsible AI also at NYU.  Her research focuses on responsible data management, fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. 

Episode Summary -

  1. The Definition of Responsible AI
  2. Example of ethical AI in the medical world - Fast MRI technology
  3. Fairness and Diversity in AI
  4. The role of regulation - What it can and can’t do
  5. Transparency, Bias in AI models and Data Protection
  6. The dangers of Gen AI Hype and problematic AI narratives from the tech industry
  7. The impotence of humans in ensuring ethical development 
  8. Why “Responsible AI” is actually a bit of a misleading term
  9. What Data & AI leaders can do to practise Responsible AI

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