Scientists have called for action to make artificial intelligence systems such as ChatGPT less racist and sexist.
Large language models (LLMs) that power AI chatbots are trained on a huge amount of data from which they learn patterns and connections between words and phrases.
This means that the language used by AI can reflect biases or discriminatory attitudes in the examples it was given.
Reuters reported in 2018 that Amazon had scrapped development of an AI system to read resumes and rate candidates after it was found to be biased against women.
And in 2020, the Home Office agreed to stop using a computer algorithm to help sort visa applications after campaigners claimed it contained “entrenched racism and bias”.
Experts from the University of Birmingham are now calling for principles of sociolinguistics – the study of language variation and change – to be taken into account when developing the revolutionary technology.
Lead author Professor Jack Grieve said: “When prompted, generative AIs such as ChatGPT may be more likely to produce negative portrayals about certain ethnicities and genders, but our research offers solutions for how LLMs can be trained in a more principled manner to mitigate social biases.
“These types of issues can generally be traced back to the data that the LLM was trained on.
“If the training corpus contains relatively frequent expression of harmful or inaccurate ideas about certain social groups, LLMs will inevitably reproduce those biases resulting in potentially racist or sexist content.”
Writing in the journal Frontiers in AI, Prof Grieve and his colleagues argued that there should be a greater focus on using more diverse examples of language, rather than just the amount of data used.
Prof Grieve added: “We propose that increasing the sociolinguistic diversity of training data is far more important than merely expanding its scale.
“For all these reasons, we therefore believe there is a clear and urgent need for sociolinguistic insight in LLM design and evaluation.
“Understanding the structure of society, and how this structure is reflected in patterns of language use, is critical to maximising the benefits of LLMs for the societies in which they are increasingly being embedded.
“More generally, incorporating insights from the humanities and the social sciences is crucial for developing AI systems that better serve humanity.”
