How societal biases are learned and amplified by models.
Bias in AI refers to the tendency of an algorithm to produce results that are systematically prejudiced due to erroneous assumptions in the machine learning process. For Large Language Models, the primary source of bias is the data they are trained on. LLMs learn from a snapshot of the internet and digitized books, which contains a vast repository of human-generated text. This text is imbued with the explicit and implicit biases, stereotypes, and societal inequities present in the real world. When the model learns to predict the next token, it also learns these statistical associations. For example, if the training data frequently associates certain professions with a particular gender (e.g., 'doctor' with 'he' and 'nurse' with 'she'), the model will learn this correlation. When prompted to complete a sentence like 'The doctor finished his shift, and...', it will be more likely to use male pronouns, reinforcing the stereotype. This can lead to significant harms. Biased models can perpetuate and even amplify societal inequalities in areas like hiring, loan applications, and criminal justice. They can generate toxic, offensive, or stereotypical content. The impact can be subtle, such as generating code with security vulnerabilities because the training data contained more examples of insecure code, or more overt, such as using derogatory language associated with specific demographic groups. Mitigating bias is a major challenge. It involves curating training data more carefully, developing techniques to 'debias' model representations during or after training, and implementing robust testing and evaluation frameworks to audit models for fairness before deployment. It is an ongoing area of research with no easy solutions.