Exploring the Modeling Mindset and the Challenges in Building Representative Models

It's the end of 2023 and our first season. The hosts reflect on what's happened with the fundamentals of AI regulation, data privacy, and ethics. Spoiler alert: a lot! And we're excited to share our outlook for AI in 2024.

• AI regulation and its impact in 2024.
  • Hosts reflect on AI regulation discussions from their first 10 episodes, discussing what went well and what didn't.
  • Its potential impact on innovation. 2:36
  • AI innovation, regulation, and best practices. 7:05
• AI, privacy, and data security in healthcare. 11:08
  • Data scientists face contradictory goals of fairness and privacy in AI, with increased interest in balancing both in 2024.
  • HIPAA privacy and the increasing use of machine learning and AI in healthcare.
  • Special thanks to the team at Shifting Privacy Left for a more in-depth discussion about privacy and synthetic data.
• AI safety and ethics in NLP research. 15:40
  • Does OpenAI's closed model set a bad precedent for research?
  • The tension in NLP research: AI safety, and OpenAI's approach 
• Modeling mindset and reality between scientists and AI experts. 18:44
  • Thanks to special guests who joined us in 2023
  • Josh Pyle and his insights on bias and actuarial bias, and how they've applied to their work. See episode 10.
  • Christoph Molnar for explaining the differences in mindset between scientists and AI scientists when modeling reality.  See episode 4.
• AI ethics and its challenges in the industry. 21:46
  • Andrew Clark emphasizes the importance of understanding the basics of time-series analysis and choosing the right tools for the job, rather than relying on automated methods or blindly using existing techniques.
  • Sid Mangalik: AI ethics discussion needs to level up, as people are confusing models with AGI and not addressing practical issues.
  • Andrew Clark: AI ethics is at a bad crossroads, with high-level discussions divorced from reality and companies paying lip service to ethical concerns.
• AI ethics and responsible development. 26:10
  • Companies with ethical bodies and practices are better equipped to handle AI ethics concerns.
  • Andrew expresses concern about the ethical implications of AI, particularly in the context of Google's Gemini project.
  • Comparing AI safety to carbon credits and the importance of a proactive approach to ethical considerations.
• AI ethics and its importance in business. 29:31
  • Susan highlights the importance of ethics in AI, citing examples of challenges between board and executive teams.

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