Recently, on my paper, I have by accident written something interesting:
"If we make an analogy to human: gpt-3.5-turbo-1106 [chatGPT] on this case specifically did not fall into confirmation bias." Source: https://www.qeios.com/read/Y13B20
Have you ever wondered on the place of biases in artificial intelligence? how much of the human biases will be passed to artificial intelligence?
Would AI be immunize from biases?
In the case of Large Language Models, human bias is definitely passed onto the model during the initial training stage, which involves processing raw text from the internet, books, etc. During fine-tuning, the models can be trained on specific domains. In the subsequent stage, reinforcement learning from human preferences is where these biases are addressed. There are excellent datasets for fine-tuning models with human preferences hosted on the platform Huggingface.co. We can always help improving these datasets or create new ones to combat bias, and that's why open source AI is so important. For instance, our dataset, H4rmony, hosted on Hugging Face addresses the ecologically damaging discourses embedded in LLMs.
Dear Jorge Vallego
"human bias is definitely passed onto the model during the initial training stage" in fact, I am not talking about biases from dataset, it is well-known and explored.
I am talking as a model. We as humans fall into biases since this is how our brain works.
As you may know, Moravec paradox is used to understand difference between human's and machine's intelligence.
So, if we provide a perfect dataset, would the model still be biases as humans, such as confirmation biases?
This is an attempt to apply the theory from Daniel Kahneman to artificial intelligence
Bias in artificial intelligence refers to the presence of unfair or prejudiced outcomes in AI systems, often reflecting existing societal biases present in the training data. It can lead to discriminatory decisions or reinforce existing disparities. Addressing bias is crucial to ensure AI systems are fair, ethical, and unbiased in their interactions and decision-making processes. Ongoing efforts within the AI community aim to mitigate bias through improved algorithms, diverse and representative datasets, and ethical considerations in AI development.
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