The Logic of the Synthetic Supplement in Algorithmic Societies
Dados Bibliográficos
| ID | 2353285 |
|---|---|
| Autores | Benjamin N Jacobsen (0000-0002-6656-8892, University of York, autor correspondente) |
| Ano | 2024 |
| Volume | 41 |
| Fascículo | 4 |
| Páginas | 41-56 |
| Data de publicação | 2024-07-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Theory Culture & Society (JOURNAL) |
| Identificadores do periódico | ISSN: 0263-2764 • E-ISSN: 1460-3616 |
| Editora | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/02632764231225768 |
| OpenAlex | W4391360014 |
| Idioma | EN |
| Citações recebidas | 9 |
| Referências citadas | 35 |
What happens when there is not enough data to train machine learning algorithms? In recent years, so-called 'synthetic data' have been increasingly used to add to or supplement the training regimes of various machine learning algorithms. Seeking to read the notion of supplementarity differently through an engagement with the work of Jacques Derrida, I propose that the nascent emergence of synthetic data embodies what I call the logic of the synthetic supplement in algorithmic societies. I argue, on the one hand, that the synthetic supplement promises and claims to resolve the ethico-political tensions, frictions, and intractabilities of machine learning. On the other hand, it always falls short of these promises because it necessarily intervenes in that which it claims to merely augment. Ultimately, this means that the gaps and frictions of machine learning cannot be completely filled, supplemented, or resolved
Computer Science · Cybernetics and Technology in Society · Digital Media and Philosophy · Ethics and Social Impacts of AI
AI, synthetic data, and the tensions of the generative gap
Fair synthetic data is not about fairness
Bourdieu Works for the Bank
Synthetic data as meaningful data. On Responsibility in data ecosystems
Governing synthetic data in the financial sector
The fabrication of synthetic data promises
Simulation and the reality gap
On algorithmic mediations
Machine Learning, Synthetic Data, and the Politics of Difference
Contagious Architecture
If…Then
The uselessness of AI ethics
A few useful things to know about machine learning
The social power of algorithms
Where fairness fails
Blindness and Insight. Essays in the Rhetoric of Contemporary Criticism
Toward a political economy of synthetic data
Critical Questions for Big Data
Machine learning political orders
Big Data, new epistemologies and paradigm shifts
On the genealogy of machine learning datasets
Machine learning and the politics of synthetic data
Racial formations as data formations
Cruel Optimism
On Deconstruction
Introduction
Doubt and the Algorithm
Critical Computation
A New Algorithmic Identity
Data Derivatives
Toward a Critique of Algorithmic Violence
Surplus Data
| Obras citantes distintas | 9 |
|---|---|
| Citações por ano | 9 |
| Intervalo de citações | 2025 - 2026 (2) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 7 |