Artificial intelligence and the conjectural sciences

Drawing on prior work in the history and philosophy of statistics, I argue that in many cases analyses powered by artificial-intelligence (AI) techniques such as machine learning (ML) are fundamentally ‘conjectural’: reliant on ex post facto abductive logics often misinterpreted in contemporary mach...

Full description

Saved in:
Bibliographic Details
Published in:BJHS Themes 2023, Vol.8, p.35-49
Main Author: Stark, Luke
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Epistemology
Historians
Hypotheses
Learning algorithms
Logic
Machine learning
Science
Scientific method
Scientists
SECTION 1: Origins? Intelligence, capture, discovery
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Drawing on prior work in the history and philosophy of statistics, I argue that in many cases analyses powered by artificial-intelligence (AI) techniques such as machine learning (ML) are fundamentally ‘conjectural’: reliant on ex post facto abductive logics often misinterpreted in contemporary machine-learning systems as reliably reproducible truth. Here I relate what Carlo Ginzburg calls ‘the conjectural sciences’ as a historical category to their contemporary instantiation in machine learning and the practice of ‘automated conjecture’. I observe how the automation of physiognomic and phrenological concepts are exemplary of the ways in which discredited conjectural pseudosciences are being revived by today's AI research. Finally, I argue that the conceptual distinction between ‘conjectural’ and ‘empirical’ science can help support contemporary efforts to regulate the design and use of AI systems by providing conceptual and historical justification for the non-development of certain classes of systems intended to automate inference.
ISSN:2058-850X
2056-354X
2056-354X
DOI:10.1017/bjt.2023.3