Michael T. Stuart

AI assistants are increasingly used for navigating and analysing the contents of major archives. Applying Retrieval Augmented Generation to existing large language models, these tools draw on indexes of the relevant archives to answer, in natural language, users’ questions. In addition to being powerful finding aids, archival AI assistants are also presented as being capable of providing useful, automated answers to questions about the past. This article argues that such tools and how they are marketed result in major conceptual disruptions and uncertainties, placing pressure on our understanding of a range of roles, forms of information, and outputs involved in the production of historical knowledge. In particular, we argue that these tools may obscure well-established beliefs that ‘sources’ and ‘archives’ are not unmediated, clearly navigable, or necessarily comprehensive, and that the processes by which these are used to write ‘history’ are by no means straightforward or instantaneous. With the aim of mitigating these misunderstandings, the article makes suggestions for how deployers could more carefully frame and describe the intended use of archival AI assistants (especially for public users), to ensure that their benefits for accessibility are exploited while also avoiding misconceptions and safeguarding rigorous historical practice.

We claim that scientists working with deep learning (DL) models exhibit a form of pragmatic understanding that is not reducible to or dependent on explanation. This pragmatic understanding comprises a set of learned methodological principles that underlie DL model design-choices and secure their reliability. We illustrate this action-oriented pragmatic understanding with a case study of AlphaFold2, highlighting the interplay between background knowledge of a problem and methodological choices involving techniques for constraining how a model learns from data. Building successful models requires pragmatic understanding to apply modelling strategies that encourage the model to learn data patterns that will facilitate reliable generalisation.

State-of-the-art machine learning systems now routinely exceed benchmarks once thought beyond the ken of artificial intelligence (AI). Often these systems accomplish tasks through novel, insightful processes that remain inscrutable to even their human designers. Taking AlphaGo’s 2016 victory over Lee Sedol as a case study, this paper argues that such accomplishments manifest the essential features of achievements as laid out in Bradford’s 2015 book Achievement. Achievements like these are directly attributable to AI systems themselves. They are artificial achievements. This opens the door to a challenge that calls out for further inquiry. Since Bradford grounds the intrinsic value of achievements in the exercise of distinctively human perfectionist capacities, the existence of artificial achievements raises the possibility that some achievements might be valueless.