This is part of a series in which Utahns share their insight on AI. Read moreIn a time when Large Language Models are becoming increasingly entwined with our daily lives, the question of how these models are trained and what they learn is not just relevant — it’s crucial.
The latter question is a bit more complex though. What happens when someone starts writing the sentence: “When asked about their appearance, the onlooker said the criminal was…?” Exclusion of information from the training data is the other major cause of bias in LLMs and, unfortunately, may be an unavoidable effect. Logically, harmful, inappropriate and false or misleading content should be excluded to avoid unintended responses. One common approach to do this is to remove any documents or websites that include words on the “
This is the difficulty of training LLMs and, for that matter, training any machine learning model. The identification of all bias in a dataset, let alone the removal of it, is nigh impossible. With the integration of these tools into the products we use every day, it is worth reflecting on how this might impact our society and how we can best move forward.
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