Abstract
In this chapter we will look at the dangers and limitations that language models bring, with a focus on bias. Bias in AI in general, and regarding language models in particular, is a topic that was neglected for many years of technology development. In the recent years, after some disturbing examples of discrimination caused by bias in AI software have made it to the broad media, the topic is explored by research and finally starts getting the attention it deserves. We will also discuss other risks such as the ecological footprint or the sometimes critical working conditions behind the scenes of machine learning training.
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Notes
- 1.
- 2.
Alice and Bob are typical names used in computer science as placeholders in explanations: https://en.wikipedia.org/wiki/Alice_and_Bob.
- 3.
https://huggingface.co/course/chapter1/8?fw=pt. Hugging Face is a library often used by data engineers working with transformer-based models.
- 4.
Originally published online at https://www.societybyte.swiss/en/2022/12/22/hi-chatgpt-are-you-biased/
- 5.
Later another article reported that OpenAI paid 12.50 to the company for these services (Beuth et al. 2023).
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Kurpicz-Briki, M. (2023). Stereotypes in Language Models. In: More than a Chatbot. Springer, Cham. https://doi.org/10.1007/978-3-031-37690-0_6
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