The Trust and Truth Dilemma

How do our psychological biases affect our ability to fact-check information shared by others?

Our psychological biases cause us to rigorously defend our own image while being less diligent in verifying the accuracy of information shared by others. This bias stems from a natural instinct for self-preservation and the desire to maintain credibility. However, when it comes to information about others, we are prone to accepting it at face value without proper verification. This can lead to the spread of misinformation, as we do not apply the same rigorous standards to external information.

What are some limitations of AI, particularly Large Language Models (LLMs)?

AI, including Large Language Models (LLMs) like ChatGPT, can give the illusion of human-like communication but fundamentally lack genuine understanding, lived experience, and common sense. LLMs are designed to predict the next word in a sentence based on vast amounts of training data, which can lead to "hallucinations"—instances where the AI generates incorrect or entirely fabricated information. These models do not distinguish between truth and fiction, and their output is only as reliable as the data they were trained on. Recognizing these limitations is crucial to avoid relying on AI for critical tasks that demand factual accuracy and understanding the potential pitfalls when AI goes wrong.

When should AI be used, and what precautions should be taken?

AI can be beneficial for creative and exploratory tasks where absolute accuracy is not critical. For example, generating interesting images, videos, or creative text can be a suitable use of AI. However, for serious work that requires precise and factual information, it is essential to double-check AI-generated content against credible sources. Tools like DeepFake-o-meter, Snopes and GPTZero can help verify and fact-check AI-generated content. These precautions ensure that you are using AI responsibly and mitigating the risks of misinformation.