Machine learning, meaning making
On reading computer science texts
Bibliographic Data
| ID | 5261008 |
|---|---|
| Authors | Louise Amoore (0000-0001-6728-8553, Durham University, corresponding author), Alexander Campolo (0000-0003-3159-4131, Durham University), Benjamin N Jacobsen (0000-0002-6656-8892, Durham University), Ludovico Rella (0000-0001-5468-9526, Durham University) |
| Year | 2023 |
| Volume | 10 |
| Issue | 1 |
| Publication date | 2023-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/20539517231166887 |
| OpenAlex | W4362014596 |
| Language | EN |
| Citations received | 20 |
| References cited | 19 |
Computer science tends to foreclose the reading of its texts by social science and humanities scholars - via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three "reading problems" - genre, readability, and meaning - we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved
Epistemology · Linguistics · Political science · Politics · Readability · Sociology · Computer Science · Ethics and Social Impacts of AI · Law · Philosophy · Artificial Intelligence
Machine unlearning and the politics of algorithmic forgetting
Assembling Topic Models
From genome to voiceome
Probabilistic spaces
What the edge tells the cloud
Deepfakes and the promise of algorithmic detectability
Simulation and the epistemology of transformer models
Close to the metal
Between fact and fairy
Between world models and model worlds
Because the twin is not a copy
Digital phantoms in medical research
Artificial intelligence, conceptions of distillation and the reframing of reasoning
Why machine learning is a misnomer
Good classification matters
Handling the hype
Testing and Errors
Politics of the prompt
The fabrication of synthetic data promises
Algorithms, AI, Big Data, and Big Tech
Science in action
My Mother Was a Computer
Machine Learners
The science question in feminism
The Black Box Society
Uncertain Archives
Laboratory Life
Postprint
The Structure of Scientific Revolutions
The alien subject of AI
Touching Feeling
Machine learning political orders
Algorithms as culture
Algorithms and their others
Interface Methods
Situated Knowledges
Algorithmic Personalization as a Mode of Individuation
| Unique citing works | 20 |
|---|---|
| Citations per year | 6,67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 18 |