AI's information can be skewed

 AI's information can be skewed

I asked ChatGPT if it's information can be skewed?

ChatGPT responded:

Yes—but it's important to distinguish intentional shaping from intentional distortion.

All major AI systems are shaped by human decisions at multiple stages. That doesn't necessarily mean they are deliberately skewed toward falsehoods, but it does mean humans influence what the model learns and how it responds.

Here are the main ways:

1. Training data selection
   • Developers decide what kinds of data to include or exclude.
   • For example, they may exclude spam, malware, explicit content, or low-quality text to improve the model.
2. Human feedback
   • After initial training, people review model responses and rate which ones are more helpful, accurate, and safe.
   • Those ratings help guide future behavior.
3. Safety rules
   • AI systems are intentionally designed not to assist with certain harmful activities, such as providing detailed instructions for violent crimes or creating malware.
   • This is a deliberate constraint on the model's behavior.
4. Handling controversial topics
   • Developers try to encourage the model to distinguish between evidence-supported claims and opinions, acknowledge uncertainty where appropriate, and avoid presenting contested claims as settled facts.
   • This is an area where reasonable people may disagree about whether a model is too cautious, not cautious enough, or biased in some direction.

Can bias enter the system?

Yes. Bias can come from several sources:

• The training data itself, which reflects human society and its imperfections.
• The people who curate the data.
• The reviewers who provide feedback.
• The choices made about safety and behavior.

Researchers actively study these issues because eliminating all bias is extremely difficult.

Is AI intentionally taught false information?

There is no evidence that mainstream AI models are generally trained to knowingly provide false information as a design goal. In fact, much of the training and evaluation effort is aimed at improving factual accuracy and reducing fabricated answers.

That said, models can still make mistakes, and they may reflect biases present in their training data or in the judgments used to guide their behavior. This is one reason it's valuable to verify important claims—especially in areas like medicine, law, science, or current events—using reliable, independent sources.

In short, human choices absolutely shape AI, but that is different from saying the model is intentionally trained to deceive. The goal is generally to make it more useful, accurate, and safe, even though those goals involve subjective design decisions and are not achieved perfectly.