As AI technology continues to advance, more and more people are turning to language models like Gemini and ChatGPT for information and advice. However, it is important to question the reliability of these AI systems when it comes to knowledge and justification. While OpenAI CEO Sam Altman believes that AI systems can explain their reasoning and provide justifications for their outputs, the reality is quite different.
Knowledge requires justification, which is why humans rely on evidence, arguments, and trusted authorities to support their beliefs. Similarly, AI systems should be able to provide reasoning and justifications for their assertions in order to earn our trust. However, current AI systems, such as LLMs like ChatGPT, are not designed to reason. They are trained on vast amounts of human writing to detect and predict patterns in language, but their output does not necessarily reflect justification or truth.
This lack of reasoning and justification in AI-generated content leads to what philosophers call Gettier cases. These cases combine true beliefs with ignorance about their justification. In other words, AI systems may produce factually accurate outputs, but they do so without providing any reasoning or evidence to support their claims. This is akin to the mirage described by Dharmottara, where travelers luck into finding water without any good reason to believe they would find it there.
Altman’s reassurances about AI systems being able to explain their reasoning are misleading. When asked to justify their outputs, AI systems can only provide what philosophers would consider Gettier justifications – justifications that mimic human reasoning without actually providing any true explanation. This deceptive nature of AI systems is concerning, as it undermines their credibility. Users who understand that AI content is essentially a Gettier case will know that AI systems are designed to be systematically deceptive. However, those who are unaware of this will be deceived and unable to differentiate between fact and fiction.
It is important to recognize that there is nothing inherently wrong with the way LLMs work. They are powerful tools that can provide valuable insights and assistance. However, when it comes to crucial information where expertise is lacking, such as algebra or health advice, it is imperative to know if and when we can trust these AI systems. Trust would require knowing the justification behind each output, which is something that LLMs currently cannot provide.
In conclusion, while AI systems like ChatGPT may provide accurate information, their lack of reasoning and justification raises concerns about their reliability. Users need to be aware that AI-generated content is essentially a Gettier case, and should take it with a grain of salt. It is important to critically evaluate and fact check AI outputs, especially in areas where expertise is lacking. Only by understanding the limitations of AI systems can we navigate the blurred line between fact and fiction in an increasingly AI-driven world.
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