Norman Stanley Fletcher and the case of the proprietary algorithmic risk assessment.

By M. Oswald, Jamie Grace. 2016

Subfields: AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

1. Arvind Narayanan
2. G. Barnes

In Social Sciences & Humanities

1. Aylin Caliskan
2. Arvind Narayanan

Citations in the corpus

• Semantics derived automatically from language corpora contain human-like biases (2016, background): “Transparency should allow courts, companies, citizen watchdogs, and others to understand, monitor, and suggest improvements to algorithms (Oswald and Grace, 2016).”
• Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality (2018, background): “15 The HART implementation represents an ideal case-study because it is one of the first operational deployments of algorithmic methods within UK policing, and is subject to ongoing assessment and validation as set out below.”