8 Risk, mitigation, and tradeoffs
This chapter covers
- Risks involved in using generative AI
- Best practices to follow when using generative AI in your analytics
- Ways to mitigate the risks and the tradeoffs involved
The rapid advancement and adoption of generative AIs in various data analytics applications have the potential to significantly improve the accuracy and efficiency of decision-making processes across numerous domains, especially since we are just starting to realize the full scope of where such models can be applied. However, along with these benefits come several risks and challenges you must carefully manage to ensure safe, responsible, and ethical use of such models.
In this chapter, we will consider the essential aspects of risk management in data analytics with generative AIs, highlighting the potential problem areas and providing guidelines for mitigating the risks we identify. While you might be reading this book for personal education about using AI models for data analytics, you may well progress to using such models in professional circumstances, using AI models to assist with data analytics in a structured and professional manner. Regardless of the size of the implementation, from personal, through educational, to large enterprise, it is crucial to have a clear view of the risks involved and to be armed with techniques to mitigate them promptly and effectively.