Algorithmic paranoia and the convivial alternative
Bibliographic Data
| ID | 5260770 |
|---|---|
| Authors | Dan Mcquillan (0000-0002-1975-1598, University of London, corresponding author) |
| Year | 2016 |
| Volume | 3 |
| Issue | 2 |
| Publication date | 2016-12-01 |
| 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 | 10.1177/2053951716671340 |
| OpenAlex | W2543522457 |
| Language | EN |
| Citations received | 24 |
| References cited | 16 |
In a time of big data, thinking about how we are seen and how that affects our lives means changing our idea about who does the seeing. Data produced by machines is most often 'seen' by other machines; the eye is in question is algorithmic. Algorithmic seeing does not produce a computational panopticon but a mechanism of prediction. The authority of its predictions rests on a slippage of the scientific method in to the world of data. Data science inherits some of the problems of science, especially the disembodied 'view from above', and adds new ones of its own. As its core methods like machine learning are based on seeing correlations not understanding causation, it reproduces the prejudices of its input. Rising in to the apparatuses of governance, it reinforces the problematic sides of 'seeing like a state' and links to the recursive production of paranoia. It forces us to ask the question 'what counts as rational seeing?'. Answering this from a position of feminist empiricism reveals different possibilities latent in seeing with machines. Grounded in the idea of conviviality, machine learning may reveal forgotten non-market patterns and enable free and critical learning. It is proposed that a programme to challenge the production of irrational pre-emption is also a search for the possibility of algorithmic conviviality
Big data · Cognitive science · Data science · Empiricism · Epistemology · Irrational number · Paranoia · Rationality · Sociology · Computer Science · Ethics and Social Impacts of AI · Neuroethics, Human Enhancement, Biomedical Innovations · Philosophy · Psychology · Artificial Intelligence
(In)direct control, freedom and the democratic imperative to refuse AI
Utopian and Dystopian Sociotechnical Imaginaries of Big Data
Lineages and Advancements in Material Culture Studies
Body, Capital, and Screens
Body, Capital and Screens
Das zweite konvivialistische Manifest
Totalizing drone gaze in tourism contexts
Relevance in Web search
On measurement of distances between texts in dictionary-based content analysis
Under the radar
A convivial-agonistic framework to theorise public service media platforms and their governing systems
Artificial intelligence
The paradoxical transparency of opaque machine learning
Digital geographies of the bug
Data Science as Machinic Neoplatonism
Algorithmic Paranoia
Raising the ideal child? Algorithms, quantification and prediction
The social imaginaries on governance through data
What are neural networks not good at? On artificial creativity
Listening without ears
Algorithms as folding
Algorithmic camouflage
Filter Bubbles? Also Protector Bubbles! Folk Theories of Zhihu Algorithms Among Chinese Gay Men
The Datafication of Health
The unreasonable effectiveness of mathematics in the natural sciences. Richard courant lecture in mathematical sciences delivered at New York University, May 11, 1959
A logical calculus of the ideas immanent in nervous activity
The perceptron
Seeing Like a State
New Media and the power politics of sousveillance in a surveillance-dominated world
The production of prediction
Crania americana, or, A comparative view of the skulls of various aboriginal nations of North and South America to which is prefixed an essay on the varieties of the human species
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
Surveillance, Snowden, and Big Data
How the machine 'thinks
Seeing Like a State
Situated Knowledges
| Unique citing works | 24 |
|---|---|
| Citations per year | 2,67 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 22 |