School of Social and Political Science

Description

Controversies in the Data Society 2026 series - Session 6

About this session

Recent developments and hype around technology grounded in natural language - in particular, large language models - has considerable-to-huge controversy about whether they can be ‘tamed’ sufficiently by engineering to suppress the way they seem to encode and seem likely to perpetuate social and economic inequalities when used in the wild. This week’s speakers are experts in natural language technology, and will explore how this may be impossible, and how we need instead to develop social processes to manage these challenges. 

Dr Adam Lopez and Dr Zee Talat will give presentations.

Dr Adam Lopez’s talk is called Large Language Models are Normal Language Technology: 

Large language models (LLMs), exemplified by ChatGPT, have sparked an overwhelming amount of conversation about their future, much of it sensationalist. I argue that the technical trajectory of LLM technology currently resembles - and will very likely continue to resemble - the past trajectory of a closely related language technology: automated translation. This is because LLMs are simply a repackaging of the same set of technical ideas, whose development spanned much of the past century (belying narratives that cast them as a result of recent ‘breakthroughs’). But automated translation has been in the wild for decades, quietly expanding its reach into new social contexts. So, critically assessing its history in these contexts should allow us to understand the range of possible impacts from LLMs. In this talk I'll sketch some of the technical and social histories of translation and discuss what I (tentatively) think they tell us about LLMs. 

Dr Zee Talat’s talk description: 

Since 2016 there has been a large body of work aimed at making language technologies, and machine learning more widely, fair and non-discriminatory. This line of work has been given an increasing amount of attention as time has passed. However, a line of work has argued that it is impossible for machine learning for human and social data - and language technologies more specifically - to be unbiased, fair, or ethical. In spite of these concerns, research and development has continued to engage with the myth of ethical language technologies. In this talk, I will argue that not only are unbiased language technologies impossible, the notion itself relies entirely on an unhuman conceptualisation of language. In this light, recent attempts towards mitigating biases in language models, and for models to act as agents with some imbued notion of what it means to be ‘ethical’ can be understood as distractions from the question of what capacity we want language technologies to act within.

About the speakers

Dr Adam Lopez is Reader in Natural Language Technologies at the School of Informatics at the University of Edinburgh. Adam is a researcher in natural language processing who has worked on many scientific, mathematical, and engineering problems related to NLP. He has held roles in both academia and industry, as an engineer, scientist, lecturer, and director of artificial intelligence at a startup.

Dr Zee Talat is a Chancellor’s Fellow in Responsible Machine Learning and Artificial Intelligence at the Centre for Technomoral Futures and the School of Informatics at the University of Edinburgh, where they lead the ZEST Lab, and also a faculty fellow at DAIR. Zee works on the intersection between machine learning, science and technology studies, and media studies with a focus on decolonial and anti-capitalist critiques of ML. Zee develops evidence that current ML methods (i.e., the mathematics of it all) are constructed in a way that naturally afford authoritarianism, colonialism, and forms of hegemony (i.e. white supremacy, patriarchy, ableism, and so on) and thinking about and developing ML methods that rethink the ways in which ML is applied to offer resistance to such notions. After a PhD at the University of Sheffield, Zee did post-docs at the Digital Democracies Institute, and Mohamed bin Zayed University of Artificial Intelligence