Large language models are powerful tools that can be used for good, but they can also introduce unintentional bias into the results. How can we use large language models for good while avoiding unintentional bias?
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We can use large language models for good while avoiding unintentional bias by using careful data selection, annotating data samples for their intended uses, training the models on diverse datasets and with diversity metrics in mind, and continuously testing and reassessing the outputs to root out unexpected biases. Additionally, it is important to consider feedback loops that may exacerbate any existing biases as well as proactively auditing results for fairness before passing them on or making decisions based upon them. 
By taking these proactive steps we can ensure that large language models deliver responsible and inclusive outcomes. 
​https://en.wikipedia.org/wiki/Bias_in_artificial_intelligence
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- "Artificial Intelligence Systems - Bias in AI" lecture notes from University of Maryland, by Professor Elina zelenhaus
assistant
Large language models are powerful tools that can be used for good, but they can also introduce unintentional bias into the results. How can we use large language models for good while avoiding unintentional bias?
user
Using large language models for good while avoiding unintentional bias requires a multi-faceted approach. First, it is important to construct large language models using a wide range of data in order to identify and mitigate potential bias in the results. Additionally, efforts should be made to diversify the development team by including individuals from various backgrounds and areas of expertise that can help ensure multiple perspectives are incorporated throughout the model’s creation. When running experiments with large language models, care must be taken to account for all potential scenarios so as to not introduce any explicit or implicit gender, racial or other types of biases into its output. Finally, transparency should be included throughout all steps of model creation; by making sure our techniques are understandable and explainable when possible, any problems can quickly identified and corrected before being used in production or released publicly.
assistant
Large language models are powerful tools that can be used for good, but they can also introduce unintentional bias into the results. How can we use large language models for good while avoiding unintentional bias?