Actionable predictions: How designers of algorithmic systems calibrate criminal futures
Hopes and fears about algorithmic predictions are often rooted in the assumption that they represent a particularly actionable form of knowledge. Algorithmic predictions, the story goes, turn historical data into anticipatory actions instantly and on a large scale. Recent empirical evidence, however...
Saved in:
| Main Authors: | Maximilian Heimstädt, Simon Egbert |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-06-01
|
| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/20539517251340636 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
How to Calibrate Your Fertilizer Spreader
by: Travis W. Shaddox, et al.
Published: (2017-11-01) -
How to Calibrate Your Fertilizer Spreader
by: Travis W. Shaddox, et al.
Published: (2017-11-01) -
AI Systems and Criminal Liability<subtitle>A Call for Action</subtitle>
by: Athina Sachoulidou
Published: (2024-10-01) -
Is your system calibrated? MRI gradient system calibration for pre-clinical, high-resolution imaging.
by: James O'Callaghan, et al.
Published: (2014-01-01) -
Scanning Micromirror Calibration Method Based on PSO-LSSVM Algorithm Prediction
by: Yan Liu, et al.
Published: (2024-11-01)