The first CAISS talk was held in September with a fabulous session from Dr Sharon Glaas on “Mitigating Researcher Bias in Linguistic Studies”.
Sharon started by defining bias as “who gets to talk and who is listened to”. For example do stay at home Mums have a voice? Sharon studies linguistics – the systematic study of language and communication – functional and descriptive not prescriptive e.g. linguistic sources of persuasion. She reminded us how the social world is studied based on how it is constructed. Some highlights:
- Linguistics frequently work in an interdisciplinary, multi disciplinary way – working with other disciplines highlights the issue of bias in ways of thinking.
- How you talk about something affects how you view it. E.g. Pro-life versus anti abortion.
- Constructivist versus positivist perspectives, the social world versus the real or natural world – what is the truth and how is meaning perceived?
- Bias is part of the world that we live in. We cannot remove it but we need to be aware of it and try to mitigate it.
- Media literacy is most important.
- One of the biggest red flags is the use of Large Language models (LLM’s) and how they are being framed. AI does not know things it just repeats them.
- People “pull down” on large chunks of language and a LLM will just predict what the next word is.
- Language is an issue as LLM’s do not learn.
Sharon also elaborated on her interesting work in a corpus assisted study of political and media discourses around the EU in the lead up to Brexit. She found that the pro –EU stance of the Guardian was systematically undermined by three key themes:
- Discourses of Conflict between UK / EU and EU / Member states
- Discourses of Disparity of citizen’s experience (EU not working)
- Discourses of Threat to the UK and an existential risk to the EU.
We all have linguistic biases – ways of conceiving and talking about things that are grounded in our world view. Sharon does not believe it is possible to entirely eliminate bias from our work – but awareness and transparency help mitigate the issue. She stressed in her conclusion that it is important to understands the impact of those biases as use of LLM’s and AI tools become more prevalent.
We had excellent feedback from Sharon’s talk, one delegate said it was “the best one hour briefing they had heard in a very long time”.
