I asked ChatGPT-4o to predict the Trump trial verdict

🌈 Abstract

The article discusses how AI models like ChatGPT can be used to predict future events, specifically the outcome of the Trump hush money trial. The author conducted experiments using ChatGPT-4 and ChatGPT-4o to generate "future narratives" about the trial's verdict, and found that across 100 trials, the models unanimously predicted a "guilty" verdict for Trump. The article explores possible reasons for this consistent prediction, including the models recognizing patterns in the legal proceedings, potential biases in the training data, and the models' tendency to select the most narratively satisfying outcome. The article concludes by cautioning that while AI can offer insights, its predictions are ultimately dependent on the data and model design, and should not replace human judgment and expertise.

🙋 Q&A

[01] Predicting the Trump Trial Verdict

1. What were the key findings of the author's experiments with ChatGPT-4 and ChatGPT-4o?

• The author conducted 100 trials, 50 with ChatGPT-4 and 50 with ChatGPT-4o, and in all 100 trials, the models unanimously predicted that Trump would be found guilty in the hush money trial.

2. What are some possible reasons the models consistently predicted a guilty verdict?

• The models may have recognized patterns in the legal proceedings that pointed towards a guilty verdict.
• The prediction could be reflective of media coverage and public opinion prevalent in the data the models were trained on, which could be skewed or incomplete.
• There could be political biases influencing the predictions, as AI responses tend to lean liberal.
• The models may have selected the most narratively satisfying outcome, which often aligns with a sense of justice being served.

3. What are the broader implications of AI's predictive capabilities discussed in the article?

• AI's predictive capabilities are more than random guesses, as they are informed estimates based on data patterns.
• However, AI predictions are ultimately dependent on the data fed into them and the model's design, and should not replace human judgment and expertise.
• The article cautions against integrating AI into areas like economics, policy, and justice without careful consideration.

[02] Limitations of AI Predictions

1. What are the key limitations of AI predictions highlighted in the article?

• AI does not "understand" context in the human sense, but operates through probabilities and pattern recognition.
• AI predictions can be influenced by biases in the training data, which may be skewed or incomplete.
• AI tends to select the most narratively satisfying outcome, which may not necessarily reflect the most likely legal outcome.
• AI predictions should be seen as complementary to, not a replacement for, human judgment and expertise.

2. How does the article suggest we should approach AI's predictive capabilities?

• The article suggests we should be cautious before integrating AI into areas like economics, policy, and justice, as AI predictions are not infallible.
• AI should be used to complement, not replace, human decision-making and expertise.
• We should be aware of the limitations and potential biases inherent in AI models when interpreting their predictions.