Protecting Your Organization from Generative AI’s Lack of Common Sense

Generative AI has made remarkable advancements in various domains, but it still lacks the basic common sense that humans possess. While it excels in certain areas, it can struggle when faced with problems that require everyday human judgment and understanding. As organizations embrace the potential of generative AI, it is crucial to protect themselves from the inherent risks associated with its limitations. This article explores the importance of introducing AI in a controlled fashion and employing techniques to mitigate these risks.

1. The Limitations of Common Sense in AI. AI, despite its impressive capabilities, does not possess the innate common sense that humans have. It can excel in well-defined tasks, such as code generation or data analysis, where clear patterns and rules exist. However, when faced with ambiguous or nuanced situations that require human judgment, AI may falter. It may struggle to provide satisfactory answers to questions about subjective matters or complex societal values.
   
2. Understanding the Problem-Specific Competence of AI. Organizations must recognize that AI’s competency varies depending on the problem at hand. While it may excel in certain tasks, it may be ill-suited for others. By understanding the strengths and limitations of generative AI, organizations can strategically apply it to problems where it can deliver the most value. This ensures that AI is leveraged effectively without compromising critical aspects that require human judgment.
   
3. Introducing AI in a Controlled Fashion. To mitigate the risks associated with generative AI’s lack of common sense, organizations should adopt a controlled approach to its implementation. This involves integrating content generation into existing workflows that involve human interventions and checks. By incorporating human oversight, organizations can ensure that outputs align with their desired standards, reducing the risk of generating inappropriate or undesirable content.
   
4. Techniques for Reduced Risk. Organizations can further minimize risk by employing techniques that ensure predictably safe outputs from generative AI. One approach is to train prompt engineers who understand how to guide AI systems to produce reliable and appropriate results. These experts can design prompts that do not rely heavily on common sense and instead focus on explicit and constrained outputs, such as generating recipes or following specific templates.
   

While generative AI offers incredible possibilities, organizations must be mindful of its limitations, particularly its lack of common sense. By introducing AI in a controlled fashion and employing techniques to reduce risk, organizations can harness its power while safeguarding against unintended consequences. Understanding the problem-specific competence of AI and incorporating human oversight are crucial in maintaining ethical and responsible AI implementation. By navigating the risks associated with generative AI, organizations can leverage its potential while ensuring alignment with their values and maintaining the trust of their stakeholders.