Can AI-generated outputs be biased?

AI-generated outputs aim to be balanced and fair, but slight biases may occur. This is because the large language models used are trained on vast datasets that reflect the internet's diverse content, which is predominantly in English and often influenced by Western perspectives. Consequently, outputs might align more closely with these cultural and linguistic nuances, and the same query could yield different responses depending on the language used. 

 The manufacturers of the models we use have made efforts to mitigate bias through techniques like data filtering (removing harmful or biased examples from the training data) and model debiasing (postprocessing the model's predictions). Furthermore, we evaluate and further steer the outputs of our AI functions to stay objective to the data and ensure output guardrails to filter out unsafe or potentially harmful responses.  

Nevertheless, the outputs produced by our AI functions are meant to be of interpretative assistance, not as a source of truth. 

 Please remember that AI-generated outputs can also help us reduce our own biases by introducing new perspectives that challenge our assumptions: “we could have incorporated AI-generated themes into our triangulation discussions to help identify oversights, alternative frames, and personal biases” (Hamilton et al., 2023, p. 13).