Lights On Data Show
The Data Governance Challenges of LLMs
Jul 21, 2023
Explore the challenges of data governance in large language learning models (LLMs) and generative AI, including privacy, bias, and intellectual property rights. Learn about implementing data governance policies, addressing bias, and the risks of loading sensitive proprietary data onto web-based LLMs. Discover different approaches to data governance and the importance of non-invasive data governance in data management.
29:55
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Large language models (LLMs) must be integrated into data governance frameworks to address challenges such as privacy, bias, and intellectual property rights effectively.
- Validating and fact-checking information generated by LLMs is crucial, requiring critical thinking skills to mitigate risks of misinformation or biased outputs, especially as future generations may lack these skills.
Deep dives
The Challenges of Large Language Models and Data Governance
Large language models (LLMs) pose challenges that are an extension of existing data governance challenges. Privacy, bias, intellectual property rights, and information sharing are concerns associated with LLMs. However, these challenges are not fundamentally different from those faced with traditional data reporting. The risks may be higher due to the widespread accessibility of LLMs, but the need for data governance programs remains the same. Data classification, protection, and governance apply to the large data sets used in LLMs to prevent bias, protect sensitive information, and ensure appropriate use. Organizations must incorporate LLMs into their data governance frameworks to address these challenges effectively.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
